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Knowledge Management Case Studies

Since researching and consulting on knowledge management from the mid-1990s, I have collected over 400 examples of knowledge management in practice in organizations large and small around the world. About 30 of these are in-depth case studies from our own research and interviews that have been published in our reports and books; around 50 are from academic journals; about 150 from conferences or articles in magazines by those involved, and the remainder are 'caselets' that illustrate the use of specific KM techniques.

As this section develops we shall pick a selection from our database and publish them in a standard format. To give a flavour of what is to follow we start with the following three examples:

• Best practices yields one free fab plant (Texas Instruments) - this is one of KM's classic examples dating from the 1990s. It demonstrates how sharing best practices across their world-wide operations Texas Instruments saved of the order of $500 million.
• Holistic KM at the Department of Health - a good example of how a KM strategy embraces several inter-related dimensions including people, processes, technology, content and also top-down, bottom-up and middle-out approaches.
• Telling Stories to Capture Lessons Learned (British Council) - the use of narrative techniques in workshops to capture lessons learned.

Other Examples

Throughout our resources, especially the articles and presentations , you will find short abstracts and references to many other cases. Here are some examples:

• Hoffman La Roche - by considering the knowledge needed to prepare clinical trials documentation for the approval authorities have contributed to faster time to market for new drugs.
• NEC - when this Japanese electronics company articulated its core knowledge base, it helped redefine the company's mission as 'computers and communications', markets in which it has shown continuing success.
• Dow Chemical - by auditing its intellectual property that was in its patent portfolio it generated significant additional revenues through licensing and other agreements.
• Skandia - in the late 1990s this Swedish based insurance company developed methods to measure it intellectual capital (the knowledge in its people and processes); this focus by its senior managers helped it grow from a small regional company to number five in the world in its market segment.
• Steelcase - this office products supplier used ethnography techniques to find how people working in offices interacted; as a result they put the lessons learned into practice in designing office environments that increased productivity through an appropriate mix of 'caves and commons' workspaces.

You'll also find examples in our archive - see the site map - cases .

Last updated: 9th April 2011

CASE STUDIES

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This newsletter was published regularly between 1993 and 2003. Its many articles still provide food for thought and practical tips for knowledge managers; you'll also find other case examples.

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Rethinking knowledge work: A strategic approach

In the half-century since Peter Drucker coined the term “knowledge workers,” their share of the workforce has steadily grown—and so has the range of technology tools aimed at boosting their productivity. Yet there’s little evidence that massive spending on personal computing, productivity software, knowledge-management systems, and much else has moved the needle. What’s more, a wide variety of recent research has begun suggesting that always-on, multitasking work environments are so distracting that they are sapping productivity. (For more on this problem, see “ Recovering from information overload .”)

After researching the productivity of knowledge workers for years, I’ve concluded that organizations need a radically different approach. Yes, technology is a vital enabler of communication, of collaboration, and of access to rising volumes of information. But least-common-denominator approaches involving more technology for all have reached a point of diminishing returns. It’s time for companies to develop a strategy for knowledge work—one that not only provides a clearer view of the types of information that workers need to do their jobs but also recognizes that the application of technology across the organization must vary considerably, according to the tasks different knowledge workers perform.

Few executives realize that there are two divergent paths for improving access to the information that lies at the core of knowledge work. The most common approach, giving knowledge workers free access to a wide variety of tools and information resources, presumes that these employees will determine their own work processes and needs. The other, the structured provision of information and knowledge, involves delivering them to employees within a well-defined context of tasks and deliverables. Computers send batches of work to employees and provide the information needed to do it.

Both the free-access and structured-provisioning approaches are in wide use, but they make radically different assumptions about how knowledge work should be performed and its productivity improved. Executives who aren’t conscious of the trade-offs they are making between them and thus don’t look for opportunities to harness the power of structure probably won’t get the most from knowledge workers.

Equally important, leaders must pursue IT and productivity opportunities at the right level of granularity. While it might be tempting to think that a given approach will work well for an entire organization, reality is rarely so tidy. In my experience, the unit of analysis should be particular jobs and roles—or at least distinct categories of jobs and roles. To move the needle in a specific business unit or function, it’s not enough to launch a set of company-wide initiatives or to count on a piece of software. Instead, leaders of knowledge workers should understand the key differences among them and tailor solutions to these peculiarities.

The free-access approach

Over the past two decades, giving knowledge workers free access to information and knowledge has been the primary way of arming them to do their jobs. The rise of the Internet, the establishment of organizational knowledge-management systems, and, most recently, the advent of social media provide knowledge workers with a vast array of information from public and private sources. More analytically focused knowledge workers may also draw upon warehouses of structured data and quantitative-analysis tools.

In this model, knowledge workers define and integrate their own information environments. The free-access approach has been particularly common among autonomous knowledge workers with high expertise: attorneys, investment bankers, marketers, product designers, professors, scientists, and senior executives, for example. Their work activities are seen as too variable or even idiosyncratic to be modeled or structured with a defined process. Their need for access to IT sources—ranging from the Internet to various online databases and social media to work tools such as e-mail, spreadsheets, presentation tools, and more complex business intelligence analytics—is presumed to be equally eclectic and unpredictable. With an increasingly porous technology barrier between personal lives and jobs, these employees can often be found doing paid work from home and tending to their personal affairs in the office.

In the free-access model, the presumption is that knowledge workers, as experts, know what information is available and can search for and manage it themselves. It’s also assumed that they have the discipline to avoid wasting time surfing the Web or watching pornography, sports, or funny YouTube videos at work. Of course, these assumptions may sometimes be incorrect.

Benefits of the free-access approach

Knowledge workers typically enjoy the free-access approach, which provides plenty of autonomy in their work processes and in how they use information. For employers, this positive feeling is probably useful for retention and job engagement.

Free access is well suited to work where it’s difficult to predict contingencies in advance. A structured-process technology would be inadequate, for example, when an investment-banking client suggests a completely novel way of structuring a transaction or, in legal settings, when a key witness becomes unavailable unexpectedly. Free-access approaches allow for creative responses to uncertainty and ambiguity.

The information technology behind the free-access model is relatively easy to implement. The Internet and social media are readily accessible to anyone, and access to third-party databases is possible with any Web browser—although closed company cultures sometimes impede knowledge sharing. Most knowledge workers know how to use basic office productivity tools, and some are even quite skilled at them. Systems integration issues are minor, since workers lie at the center of the information flow.

Shortcomings of the free-access approach

The problems of free access are fairly obvious: while workers may know how to use technology tools, they may not be skilled at searching for, using, or sharing the knowledge. One survey revealed that over a quarter of a typical knowledge worker’s time is spent searching for information. 1 1. Jeff Dance, “Enterprise technology delivers more efficiency (4 of 10),” freshconsulting.com, December 9, 2009. Another found that only 16 percent of the content within typical businesses is posted to locations where other workers can access it. 2 2. “Managers say the majority of information obtained for their use is worthless, Accenture survey finds,” accenture.com, January 4, 2007. Most knowledge workers haven’t been trained in search or knowledge management and have an incomplete understanding of how to use data sources and analytical tools.

Productivity losses can be substantial. Even before the advent of social media, workers in one 2005 survey sponsored by America Online and Salary.com cited personal Internet use as the biggest distraction at work. Another study of workplace productivity found that average knowledge workers access their e-mail more than 50 times, use instant messaging 77 times, and visit more than 40 Web sites a day. 3 3. Tony Wright, “Information overload: Show me the data,” blog.rescuetime.com, June 14, 2008. A UK study suggests that social-media use by knowledge workers costs British companies £6.5 billion a year in lost productivity. 4 4. “Facebook costs UK billions,” GSS Monthly Newsletter , www.gss.co.uk, February 2008.

Productivity metrics are nearly nonexistent. If productivity is measured at all, it’s only at the highest level, such as legal briefs developed per month, research articles written and published per year, or new drug compounds discovered per decade. Fine-grained monitoring of productivity and information would, of course, help to improve productivity but risks clashing with the spirit of free information access.

The structured provision of knowledge

Structured-provision technologies first appeared in the early 1990s and have improved considerably of late. They often have a range of functions. The most important is workflow technology that controls how knowledge workers get information and job tasks. These workers may encounter supporting technologies that include information portals, business rules or algorithms to automate decisions, document- or content-management systems, business process management-and-monitoring systems, and collaboration tools. Increasingly modular component designs make these technologies easier to deploy.

In corporate parlance, such technologies are often called case-management systems because they allow workers to complete an entire case or unit of work. Such applications include the processing of legal cases, insurance claims, or bank loans; the issuing of permits or licenses; and the completion of interactions with patients in health care. Case management can create value whenever some degree of structure or process can be imposed upon information-intensive work. Until recently, structured-provision approaches have been applied mostly to lower-level information tasks that are repetitive, predictable, and thus easier to automate.

Benefits of the structured model

Productivity is the major benefit: as measured by the completion of key tasks per unit of work time, it often rises by 50 percent when organizations implement these technologies. One automobile-leasing company, for example, achieved such gains after it implemented a new system for lease processing and end-of-lease sale offers. The reason for the improvement was that workers had few distractions and spent no time searching for information.

Adding to the efficiencies, in most cases companies can route tasks globally to any worker with the time and expertise to undertake them; if Sally is away on vacation, the system knows and sends cases to Joe for approval instead. Work processes become more transparent, and it becomes easier to manage them, to exercise approval authority, and to monitor improvements. The structured model also facilitates collaboration and the coordination of tasks. Many implementations help companies engage multiple workers and groups to process cases. These systems also often incorporate business rules or algorithms, determined by an organization’s best experts, that help companies decide, say, whether to issue policies, make loans, or pay claims. For managers, these systems can therefore improve the quality and consistency of decision making, while also speeding it up through automation or semiautomation.

Shortcomings of the structured model

The downside of these technologies is negative reactions by the workers who use them. Some managers I have interviewed say that workers feel there is too much structure and too little autonomy in their work; they sometimes feel “chained to their desks.” Socialization at work—informal chats in the hallway—can decrease dramatically. In some cases where workers previously had a high degree of autonomy (physicians at an academic medical center, for example), they revolted against such systems. Some organizations that encountered initial resistance found that it decreased over time. Other organizations overcame workers’ objections by instituting new forms of social interaction that meshed with improved work processes.

In structured information environments, computer systems rather than knowledge workers integrate the work, so extensive system and process design is required up front for implementation. While these systems can be tailored to fit complex business processes, that kind of tight fit can become a problem if business environments or processes change. When the system includes an automated decision-making component, it’s important to monitor the business environment and the outcome of decisions to ensure that the system continues to produce the desired process output. One chilling example of how things can go awry: automated but insufficiently monitored mortgage decisions were among the contributors to the recent financial crisis.

How companies apply these principles

The greatest potential for productivity improvements involves bringing more structured knowledge to workplaces and processes where the free approach has dominated. So far, lower-level process work has been the primary beneficiary of structured-provision tools. However, advancing technologies are making them better suited to tasks that until now have been the preserve of free-access approaches—tasks centered on expert thinking and collaboration. In one example, a major academic medical center is employing “smart forms” that present physicians with all the available information about a particular patient’s disease on one screen and even produce first drafts of notes about their interactions with patients for medical records.

Some forward-looking companies are testing more structured approaches in a broader range of work, often with positive results. Here are three areas of progress.

High-level work

Companies have considerable opportunity for applying structured technology and processes to the more routine aspects of even highly collaborative jobs. An insurance company, for example, implemented workflow- and document-management technologies to help develop and modify its investment portfolio. The system replaced numerous spreadsheets and e-mails with a common global system that synchronized communications and transactions among several different groups across several countries. Each group (including operations, funding, controls, and legal) now adds its components to the portfolio. When a new portfolio or modification is completed, the documents are finalized and sent to an external custodian for management and recording. Fund managers find the system relatively noninvasive; if their involvement is needed for a decision or approval, they are notified automatically via e-mail.

Better processes

Technologies are also being used to structure previously unstructured processes. For example, GE Energy Financial Services, which specializes in lending for large energy projects, has worked to boost the productivity and quality of decisions in its loan underwriting. A managing director with responsibilities for the unit’s marketing and investment strategy brought together GE analysts and researchers, who extracted typical decision rules from experienced company executives. The rules were embedded in a semiautomated decision system that scores prospective deals and recommends that they be approved or disapproved. Junior analysts can use the system to determine whether a deal is likely to succeed—without taking it to a credit committee comprising senior business unit executives, who can of course override the recommendation if they wish to do so. Deals made using the new approach have generated returns 40 percent higher than the old, unstructured one did.

Hybrid approaches

Some organizations combine the free and structured approaches. One of the easiest ways of doing so is to place partial restrictions on the types of information highly autonomous workers can use—for example, by limiting access to pornography, sports, or social-networking sites while at work. A more nuanced approach allows employees to be both free and structured. Partners Healthcare, which comprises several teaching hospitals in Boston, has a structured system that automatically recommends appropriate drugs and treatments to physicians but allows them to override it. The organization also makes a variety of free-access knowledge databases available to doctors, but the structured system, which incorporates medical knowledge into the process for ordering care, is used much more frequently.

A related approach imposes structured techniques for only some aspects of a job. Some companies, for example, use product-lifecycle-management systems to structure the back end of the product design process but don’t use them during the early product conceptualization and brainstorming stages. The key issue here is to decide which aspects of the relevant process could benefit from more structured technologies and processes and which should be left largely untouched by them.

Crafting a strategy for knowledge work

Few organizations have thought systematically about where additional structure could enhance productivity. A good starting point is identifying your knowledge workers and understanding the range of tasks they perform. The unit of analysis should be a particular knowledge job, not the organization as a whole. That’s important because different types of knowledge workers within the same organization often have very different knowledge and information requirements. Furthermore, knowledge is more readily structured for some jobs than for others, and some workers can resist imposed structures more than others.

Matching technology and work

I have found the matrix in the exhibit very useful when planning technology strategies for knowledge workers. It is based on my experience that knowledge work generally falls into one of four clusters, each with its own characteristics. These four knowledge work classifications are shaped by two factors: the work’s degree of complexity ( x -axis) and the level of interdependence among workers who carry out a task ( y -axis). Leaders can use this taxonomy as a guide to determine whether a structured, free, or hybrid approach best fits a given job.

The transaction cell of the matrix describes knowledge work requiring relatively low amounts of collaboration and judgment, such as employment in call centers, claims processing, and other administrative-intensive roles. Structured-provision approaches fit this type of work well—indeed, it is the only type where they are commonly applied. One example is a call center system that channels calls from customers to workers, along with all the information and knowledge needed to meet the customers’ needs. Another would be an insurance-claims-processing system that delivers all necessary documents and forms to claims workers.

As the degree of collaboration required for a job moves up into the exhibit’s integration cell, free-access tools become widely available. It is common to find work circulating by way of e-mail and voluntary collaboration and much less common to find structured-provision technologies. Yet there are some semistructured exceptions, including lower-level roles in software development, engineering, and product design and development. The aforementioned product-lifecycle-management system that tracks designs, components, and approvals might help structure the work of certain engineers, for example.

In the exhibit’s expert cell, the goal is to apply expert knowledge to tasks or problems. The relevant knowledge traditionally is stored in the expert’s brain, but today many organizations want to supplement it with online knowledge. Although free-access technologies are typically the chief means of accessing it, in some instances structured approaches can be applied, particularly when productivity and online-knowledge access are equally important. In such cases, the organization must find some way for a computer to mediate the expert’s job, so that knowledge can be embedded in the flow of the work process, as some health care organizations have done with intelligent order entry systems for providers. Similarly, a few leading IT-consulting firms are attempting to bring more structure to the delivery of various IT services by using online tools. Expert jobs may also benefit from “guided” data-mining and decision analysis applications for work involving quantitative data: software leads the expert through the analysis and interpretation of data.

Finally, work in the exhibit’s collaboration cell—which involves knowledge activities such as those of investment bankers crafting big deals, financial analysts creating corporate plans and budgets, marketers developing major marketing plans, attorneys working in teams on large cases, and scientists playing a part in large scientific projects—is usually iterative and unstructured. Typically, the only tools that succeed in such environments provide free access to information and are used voluntarily by the worker. Although systems involving structured workflows and embedded knowledge aren’t entirely beyond the scope of this kind of work, they will be hard to develop. Exceptions might include areas such as knowledge reuse: a group of collaborating attorneys, for example, could recycle a legal brief.

Mastering common challenges

While the classification of work and roles will vary considerably across organizations, the pursuit of productivity through structure typically brings with it at least two common challenges. These are preventing the alienation of formerly free knowledge workers and avoiding automated crack-ups like the ones some financial-services firms experienced with mortgage approvals.

Allowing knowledge workers to override automated or semiautomated decisions can help alleviate both of these problems. Such measures can not only lead to better decisions but also reduce resentment or even rebellion against the system. Of course, if experts constantly override it, you must find out why.

Another way of smoothing the path to structure is letting knowledge workers use familiar, typically free-access tools when they interact with a structured system. To alert them when it’s time to use a structured application, for example, have it send them an e-mail. If a structured task requires, say, passing financial information to and from the system, let workers use a spreadsheet. Always remember: high-end knowledge workers don’t want to spend all their working hours interacting with automated tools.

Finally, it’s critical to ensure that at least some knowledge workers and executives understand how the structured system works, so they can be alert for signs that it is out of kilter with changes in the economic environment or business model. Identifying such mismatches will help organizations know when they should pull the plug on structured systems and return to human judgment—a return that can save them from losing lots of money, fast.

We live in a world where knowledge-based work is expanding rapidly. So is the application of technology to almost every business process and job. But to date, high-end knowledge workers have largely remained free to use only the technology they personally find useful. It’s time to think about how to make them more productive by imposing a bit more structure. This combination of technology and structure, along with a bit of managerial discretion in applying them to knowledge work, may well produce a revolution in the jobs that cost and matter the most to contemporary organizations.

Thomas Davenport, an alumnus of McKinsey’s New York office, holds the president’s chair in information technology and management at Babson College.

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Knowledge Management Practices in India: a Case Study of a Premier B-School

• Published: 02 February 2022
• Volume 14 , pages 905–923, ( 2023 )

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• Ayesha Khatun 1 ,
• Roktim Sarmah 1 &
• Sajad Nabi Dar   nAff1  

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The B-School under study is one of the top ten B-Schools in India and the number one B-School in terms of pubic university departments. The school has been performing excellently well in terms of placements and publication which are related to knowledge management practices. This research was undertaken with an attempt to analyse the knowledge management practices, its issues and challenges, and the remedial strategies adopted by the school. For carrying out the investigation, the case study method of research was applied. Both the primary and secondary sources of data were used for answering the research question. The basic information about the selected B-School was collected from secondary sources including the websites of the concerned B-School and the schools’ published Information Brochures among others. For the collection of primary data, the respondents were taken from full time faculty members and employees representing the administration department. The findings of the study show that the school has been doing very well in certain parameters including academic freedom of faculty members and student, knowledge dissemination in terms of publication, quality of work-life, and curriculum flexibility. However, the areas like a faculty exchange program, internal financial support for research and development, and administrative support and guidance for R&D works scored relatively less, whereas other parameters were found to be performing satisfactorily. Implications for the education industry are to ensure collection, processing, sharing, and ease in archival of data for enhancement of knowledge management practices and its application. The present study is first of its kind so far as knowledge management practices in B-School in India are concerned. It contributes immensely to the literature of knowledge management with reference to B-Schools.

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Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behaviour and Human Decision Process, 82 (1), 150–169.

Article   Google Scholar  

Arora, R. (2002). Implementing knowledge management: A balanced score card approach, Journal of Knowledge Management (Emerald) , 6 , 3.

Brelade, S., & Harman, C. (2006). A practical guide to knowledge management . Viva Books Private Ltd.

Google Scholar  

Carrion, G. (2006). Competitive advantage of knowledge management. In D.G. Scwartz (Ed.), Encyclopedia of knowledge management . (pp. 34–43). Hershey, P.A: Idea Group Research.

Carroll, J., Choo, C. D., Isenhour, P., Kerr, S., Maclean, A., & Rosson, M. (2003). Knowledge management support for teachers. Educational Technology Research & Development, 51 (4), 42–64.

Chatterjee, S. (2014). Managing constraints and removing obstacles to knowledge Management. The IUP Journal of Knowledge Management, Vol XII, No., 4 , 24–39.

Chaudhary, H. C. (2005). Knowledge management for competitive advantage . Excel Books.

Chen, L., & Mohamed, S. (2007). Empirical approach to understand the knowledge management process. In M. Ahmed, S. Azhar, & M. Sherif (Eds.), Accelerating innovation in engineering, management and technology (pp. 525–532). Florida International University, Gold Coast.

Chiang, H. H. (2011). The relationship between high commitment HRM and knowledge sharing behaviour and its mediators. International Journal of Manpower (emerald), 32 (5/6), 604–622.

Choi, Y. S. (2000). An empirical study of factors affecting successful implementation of knowledge management, University of Nebraska. Retrieved on 12 th March 2016 from  https://search.proquest.com/hnttimesofindia/docbiew/304612599 .

Cranfield, D. J., & Taylor, J. (2008). Knowledge management and higher education: A UK case study. Electronic Journal of Knowledge Management, 6 (2), 85–100.

Davenport, T. H., & Prusak, L. (2000). Working knowledge: How organizations manage what they know . Harvard Business School Press.

Davenport, T. (2007). Managing knowledge to fuel growth . Harvard Business School Press.

Dhamdhere, S. N. (2015). Knowledge management model for higher educational institutes. Journal of Commerce and Management Thought, 6 (1), 162–183.

Doctor, G. (2008). Capturing intellectual capital with an institutional repository at a business school in India. Library Hi-Tech, 26 (1), 110–125.

Dunford, R. (2000). Key challenges in the search for the effective management of knowledge in management consulting firms. Journal of Knowledge Management (Emerald), 4 (4), 295–302.

Edwards, J. (2011). A process view of knowledge management: It aint what you do, it’s the way that you do It. The Electronic Journal of Knowledge Management,   9 (4), 297–306.

Geisler, E., & Wickramasinghe, N. (2009). Principles of knowledge management-theory, practice and cases . M E Sharp.

Gettier, E. (1963). Is justified true belief knowledge? Retrieved March 28, 2016, from http://www-bcf.usc.edu/~kleinsch/Gettier.pdf .

Gupta, B., Iyer, L. S., & Aronson, J. E. (2000). Knowledge management practices and challenges. Industrial Management and Data Systems, 100 (1), 17–21.

Gurteen, D. (1999). Creating a knowledge sharing culture. Knowledge Management Magazine, 2 (5), 1-7.

Hansen, M., Nohria, N., & Tierney,. (1999). What’s your strategy for managing knowledge . Harvard Business School Press.

Hislop, D. (2005). Knowledge management in organizations: A critical introduction . Oxford University Press.

Jo, S. J., & Joo, K. (2011). Knowledge sharing: The influences of learning, organizational culture, organizational commitment and organizational citizenship behaviour. Journal of Leadership and Organizational Studies, 18 (3), 353–364.

Kamasak, R., & Yavuz, M. (2016). The relative contribution of different knowledge management process on innovation performance. The 58th IIER International Conference , (pp. 9–13). Dubai, UAE.

Kambil, A. (2009). Obliterate knowledge management: Everyone is a knowledge manager. Journal of Business Strategy, 30 (6), 66–68.

Lelic, S. (2002). The Knowledge. Knowledge Management Magazine, 6 (1), 1–3.

Liebowitz, J. (1999). Key ingredients to the success of an organization’s knowledge management strategy. Knowledge and Process Management, 6 (1), 37–40.

Madan, P., & Khanka, S. (2010). Contribution of knowledge management practices in creating sustainable competitive advantage for business schools in India. Journal of Information and Knowledge Management, 9 (4), 387–397.

Mageswari, S. D., Sivasubramanian, C., & Dath, T. N. S. (2016). The impact government initiatives on knowledge management processes: An empirical analysis, The IUP Journal of Knowledge management , XIV (1), 7–32.

Maheshwari, S. P. (2016). Management education: Current scenario in India. IOSR Journal of Business and Management, 3 (37), 66–70.

Neumann, G., & Tome, E. (2011). The changing role of knowledge in companies: How to improve business performance through knowledge. The Electronic Journal of Knowledge Management,   9 (1), 73–84.

Omotayo, F. (2015). Knowledge management as an organizational management: A review of literature. Library Philosophy and Practice, 1 (23), 1–23.

Porter, M. E. (1998). Clusters and the new economics of competition, Harvard Business Review ( November-December), 77–90.

Raman, A. T. (2003). Knowledge management — A resource book” . Excel Books.

Rasula, J., Vuksic, B. V., & Stemberger, I. M. (2012). The impact of knowledge management on organizationalperformance. Economic and Business Review, 14 (2), 147–168.

Roy, M., Chatterjee, T., & Linnanen, L. (2012). Exploring effective knowledge management to improve organizational performance in globalised environment: An empirical study. Advances in Management and Applied Economics, 2 (1), 205–213.

Russell, B. (1948). Human knowledge — Its scope and limits”. George Allen & Unwin Ltd.: London.

Sajeva, S., & Jucevicious, R. (2010). Determination of essential knowledge management system components and their parameters. Social Sciences, 1 (67), 80–90.

Saxena, R., & Tiwari, A. (2013). Knowledge management — The key driver for growth”. Global Journal of Enterprise Information System, 5 (2), 59–65.

Sharp, D. (2003). Knowledge management today: Challenges and opportunities. Information Sytems,  32–37.

Shukla, R. (2012). Knowledge management in higher education. International Journal of Reviews, Surveys and Research, 1 (2), 23–39.

Sinha, A. (2014). KM in academic libraries. Journal of Indian Research, 2 (1), 123–125.

Sveiby, K. E. (1996). Retrieved on March 28, 2016, from www.seviby.com ; http://www.sveiby.com/articles/KnowledgeManagement.html .

Sveiby, K. E. (2015). Retrieved on 29 August 2017, from www.sveiby.com : http://www.sveiby.com/articles/vikings.htm . 1–5.

Tarnekar, S. (2017). Application of knowledge management to curriculum development process. Journal of Commerce and Management Thought, 8 (3), 383–386.

Thakur, V., & Sinha, S. (2013). Knowledge management in an Indian perspective. The SIJ Transactions on Industrial, Financial and Business Management, 1 (1), 7–12.

Vashisth, R., & Mehta, A. (2013). Factors influencing knowledge management in Indian B-schools: An empirical evidence. The IUP Journal of Knowledge Management,   XI (2), 19–45.

Vashisth, R., Kumar, R., & Chandra, A. (2010). Barriers and facilitators to knowledge management: Evidence from selected Indian universities. The IUP Journal of Knowledge Management,   VIII (4), 7–24.

White, N. P. (1976). Plato on knowledge and reality (p. 254). Hackett Publishing Co.

Wilson, T. D. (2002). The nonsense of knowledge management. Information Research, 8 (1), 1–12.

Zhao, J., & Pablos, P. O. D. (2011). Regional knowledge management: The perspective of management theory. Behaviour and Information Technology, 30 (1), 39–49.

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Sajad Nabi Dar

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Ayesha Khatun & Roktim Sarmah

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Khatun, A., Sarmah, R. & Dar, S.N. Knowledge Management Practices in India: a Case Study of a Premier B-School. J Knowl Econ 14 , 905–923 (2023). https://doi.org/10.1007/s13132-022-00923-0

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Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.

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 In today’s digital age, learning management systems (LMS) play a crucial role in delivering training programs to employees. However, a lack of product knowledge can hinder the smooth sharing and access of learning content. In this blog article, we will explore a real-life case study where a shared catalog issue was successfully resolved, highlighting the steps taken to troubleshoot and resolve the problem.

Case Study: Recently, an admin of ALM encountered an issue while sharing a learning program with another admin of another ALM instance via the Catalog. ​ The admin reported that they were unable to find the shared learning program in their instance of the acquired Catalog, and it appeared to have no content. ​

Upon receiving the report, a senior technical support engineer from Adobe Learning Manager (ALM) promptly acknowledged the issue and initiated the investigation process.

​Taking immediate action, the support engineer enabled the feature, ensuring that the learning path would become visible to the admin and learners of the destination ALM account.

Following the resolution steps, the support engineer communicated the findings to the user. They confirmed that the learning path should now be visible in the Catalog. ​ The admins verified the successful resolution and expressed their gratitude for the prompt assistance.

Conclusion: This case study exemplifies the importance of efficient troubleshooting and collaboration between users and technical support teams in resolving shared catalog issues. By promptly acknowledging the problem, gaining necessary access, and identifying the root cause, the support engineer successfully resolved the issue and ensured that the learning program became accessible to the user.

By sharing this case study, we hope to shed light on the troubleshooting process involved in resolving shared catalog issues.

Remember, if you encounter any issues with shared catalogs or any other technical problems, don’t hesitate to reach out to your LMS support team. They are there to assist you and ensure that your learning journey remains uninterrupted.

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• Published: 09 May 2024

Evaluation of integrated community case management of the common childhood illness program in Gondar city, northwest Ethiopia: a case study evaluation design

• Mekides Geta 1 ,
• Geta Asrade Alemayehu 2 ,
• Wubshet Debebe Negash 2 ,
• Tadele Biresaw Belachew 2 ,
• Chalie Tadie Tsehay 2 &
• Getachew Teshale 2  

BMC Pediatrics volume  24 , Article number:  310 ( 2024 ) Cite this article

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Integrated Community Case Management (ICCM) of common childhood illness is one of the global initiatives to reduce mortality among under-five children by two-thirds. It is also implemented in Ethiopia to improve community access and coverage of health services. However, as per our best knowledge the implementation status of integrated community case management in the study area is not well evaluated. Therefore, this study aimed to evaluate the implementation status of the integrated community case management program in Gondar City, Northwest Ethiopia.

A single case study design with mixed methods was employed to evaluate the process of integrated community case management for common childhood illness in Gondar town from March 17 to April 17, 2022. The availability, compliance, and acceptability dimensions of the program implementation were evaluated using 49 indicators. In this evaluation, 484 mothers or caregivers participated in exit interviews; 230 records were reviewed, 21 key informants were interviewed; and 42 observations were included. To identify the predictor variables associated with acceptability, we used a multivariable logistic regression analysis. Statistically significant variables were identified based on the adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value. The qualitative data was recorded, transcribed, and translated into English, and thematic analysis was carried out.

The overall implementation of integrated community case management was 81.5%, of which availability (84.2%), compliance (83.1%), and acceptability (75.3%) contributed. Some drugs and medical equipment, like Cotrimoxazole, vitamin K, a timer, and a resuscitation bag, were stocked out. Health care providers complained that lack of refreshment training and continuous supportive supervision was the common challenges that led to a skill gap for effective program delivery. Educational status (primary AOR = 0.27, 95% CI:0.11–0.52), secondary AOR = 0.16, 95% CI:0.07–0.39), and college and above AOR = 0.08, 95% CI:0.07–0.39), prescribed drug availability (AOR = 2.17, 95% CI:1.14–4.10), travel time to the to the ICCM site (AOR = 3.8, 95% CI:1.99–7.35), and waiting time (AOR = 2.80, 95% CI:1.16–6.79) were factors associated with the acceptability of the program by caregivers.

Conclusion and recommendation

The overall implementation status of the integrated community case management program was judged as good. However, there were gaps observed in the assessment, classification, and treatment of diseases. Educational status, availability of the prescribed drugs, waiting time and travel time to integrated community case management sites were factors associated with the program acceptability. Continuous supportive supervision for health facilities, refreshment training for HEW’s to maximize compliance, construction clean water sources for HPs, and conducting longitudinal studies for the future are the forwarded recommendation.

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Integrated Community Case Management (ICCM) is a critical public health strategy for expanding the coverage of quality child care services [ 1 , 2 ]. It mainly concentrated on curative care and also on the diagnosis, treatment, and referral of children who are ill with infectious diseases [ 3 , 4 ].

Based on the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF) recommendations, Ethiopia adopted and implemented a national policy supporting community-based treatment of common childhood illnesses like pneumonia, Diarrhea, uncomplicated malnutrition, malaria and other febrile illness and Amhara region was one the piloted regions in late 2010 [ 5 ]. The Ethiopian primary healthcare units, established at district levels include primary hospitals, health centers (HCs), and health posts (HPs). The HPs are run by Health Extension Workers (HEWs), and they have function of monitoring health programs and disease occurrence, providing health education, essential primary care services, and timely referrals to HCs [ 6 , 7 ]. The Health Extension Program (HEP) uses task shifting and community ownership to provide essential health services at the first level using the health development army and a network of woman volunteers. These groups are organized to promote health and prevent diseases through community participation and empowerment by identifying the salient local bottlenecks which hinder vital maternal, neonatal, and child health service utilization [ 8 , 9 ].

One of the key steps to enhance the clinical case of health extension staff is to encourage better growth and development among under-five children by health extension. Healthy family and neighborhood practices are also encouraged [ 10 , 11 ]. The program also combines immunization, community-based feeding, vitamin A and de-worming with multiple preventive measures [ 12 , 13 ]. Now a days rapidly scaling up of ICCM approach to efficiently manage the most common causes of morbidity and mortality of children under the age of five in an integrated manner at the community level is required [ 14 , 15 ].

Over 5.3 million children are died at a global level in 2018 and most causes (75%) are preventable or treatable diseases such as pneumonia, malaria and diarrhea [ 16 ]. About 99% of the global burden of mortality and morbidity of under-five children which exists in developing countries are due to common childhood diseases such as pneumonia, diarrhea, malaria and malnutrition [ 17 ].

In 2013, the mortality rate of under-five children in Sub-Saharan Africa decreased to 86 deaths per 1000 live birth and estimated to be 25 per 1000live births by 2030. However, it is a huge figure and the trends are not sufficient to reach the target [ 18 ]. About half of global under-five deaths occurred in sub-Saharan Africa. And from the top 26 nations burdened with 80% of the world’s under-five deaths, 19 are in sub-Saharan Africa [ 19 ].

To alleviate the burden, the Ethiopian government tries to deliver basic child care services at the community level by trained health extension workers. The program improves the health of the children not only in Ethiopia but also in some African nations. Despite its proven benefits, the program implementation had several challenges, in particular, non-adherence to the national guidelines among health care workers [ 20 ]. Addressing those challenges could further improve the program performance. Present treatment levels in sub-Saharan Africa are unacceptably poor; only 39% of children receive proper diarrhea treatment, 13% of children with suspected pneumonia receive antibiotics, 13% of children with fever receive a finger/heel stick to screen for malaria [ 21 ].

To improve the program performance, program gaps should be identified through scientific evaluations and stakeholder involvement. This evaluation not only identify gaps but also forward recommendations for the observed gaps. Furthermore, the implementation status of ICCM of common childhood illnesses has not been evaluated in the study area yet. Therefore, this work aimed to evaluate the implementation status of integrated community case management program implementation in Gondar town, northwest Ethiopia. The findings may be used by policy makers, healthcare providers, funders and researchers.

Method and material

Evaluation design and settings.

A single-case study design with concurrent mixed-methods evaluation was conducted in Gondar city, northwest Ethiopia, from March 17 to April 17, 2022. The evaluability assessment was done from December 15–30, 2021. Both qualitative and quantitative data were collected concurrently, analyzed separately, and integrated at the result interpretation phase.

The evaluation area, Gondar City, is located in northwest Ethiopia, 740 km from Addis Ababa, the capital city of the country. It has six sub-cities and thirty-six kebeles (25 urban and 11 rural). In 2019, the estimated total population of the town was 338,646, and 58,519 (17.3%) were under-five children. In the town there are eight public health centers and 14 health posts serving the population. All health posts provide ICCM service for more than 70,852 populations.

Evaluation approach and dimensions

Program stakeholders.

The evaluation followed a formative participatory approach by engaging the potential stakeholders in the program. Prior to the development of the proposal, an extensive discussion was held with the Gondar City Health Department to identify other key stakeholders in the program. Service providers at each health facility (HCs and HPs), caretakers of sick children, the Gondar City Health Office (GCHO), the Amhara Regional Health Bureau (ARHB), the Minister of Health (MoH), and NGOs (IFHP and Save the Children) were considered key stakeholders. During the Evaluability Assessment (EA), the stakeholders were involved in the development of evaluation questions, objectives, indicators, and judgment criteria of the evaluation.

Evaluation dimensions

The availability and acceptability dimensions from the access framework [ 22 ] and compliance dimension from the fidelity framework [ 23 ] were used to evaluate the implementation of ICCM.

Population and samplings

All under-five children and their caregivers attended at the HPs; program implementers (health extension workers, healthcare providers, healthcare managers, PHCU focal persons, MCH coordinators, and other stakeholders); and ICCM records and registries in the health posts of Gondar city administration were included in the evaluation. For quantitative data, the required sample size was proportionally allocated for each health post based on the number of cases served in the recent one month. But the qualitative sample size was determined by data saturation, and the samples were selected purposefully.

The data sources and sample size for the compliance dimension were all administrative records/reports and ICCM registration books (230 documents) in all health posts registered from December 1, 2021, to February 30, 2022 (three months retrospectively) included in the evaluation. The registries were assessed starting from the most recent registration number until the required sample size was obtained for each health post.

The sample size to measure the mothers’/caregivers’ acceptability towards ICCM was calculated by taking prevalence of caregivers’ satisfaction on ICCM program p  = 74% from previously similar study [ 24 ] and considering standard error 4% at 95% CI and 10% non- responses, which gave 508. Except those who were seriously ill, all caregivers attending the ICCM sites during data collection were selected and interviewed consecutively.

The availability of required supplies, materials and human resources for the program were assessed in all 14HPs. The data collectors observed the health posts and collected required data by using a resources inventory checklist.

A total of 70 non-participatory patient-provider interactions were also observed. The observations were conducted per each health post and for health posts which have more than one health extension workers one of them were selected randomly. The observation findings were used to triangulate the findings obtained through other data collection techniques. Since people may act accordingly to the standards when they know they are observed for their activities, we discarded the first two observations from analysis. It is one of the strategies to minimize the Hawthorne effect of the study. Finally a total of 42 (3 in each HPs) observations were included in the analysis.

Twenty one key informants (14 HEWs, 3 PHCU focal person, 3 health center heads and one MCH coordinator) were interviewed. These key informants were selected since they are assumed to be best teachers in the program. Besides originally developed key informant interview questions, the data collectors probed them to get more detail and clear information.

Variables and measurement

The availability of resources, including trained healthcare workers, was examined using 17 indicators, with weighted score of 35%. Compliance was used to assess HEWs’ adherence to the ICCM treatment guidelines by observing patient-provider interactions and conducting document reviews. We used 18 indicators and a weighted value of 40%.

Mothers’ /caregivers’/ acceptance of ICCM service was examined using 14 indicators and had a weighted score of 25%. The indicators were developed with a five-point Likert scale (1: strongly disagree, 2: disagree, 3: neutral, 4: agree and 5: strongly agree). The cut off point for this categorization was calculated using the demarcation threshold formula: ( \(\frac{\text{t}\text{o}\text{t}\text{a}\text{l}\, \text{h}\text{i}\text{g}\text{h}\text{e}\text{s}\text{t}\, \text{s}\text{c}\text{o}\text{r}\text{e}-\,\text{t}\text{o}\text{t}\text{a}\text{l}\, \text{l}\text{o}\text{w}\text{e}\text{s}\text{t} \,\text{s}\text{c}\text{o}\text{r}\text{e}}{2}) +total lowest score\) ( 25 – 27 ). Those mothers/caregivers/ who scored above cut point (42) were considered as “satisfied”, otherwise “dissatisfied”. The indicators were adapted from the national ICCM and IMNCI implementation guideline and other related evaluations with the participation of stakeholders. Indicator weight was given by the stakeholders during EA. Indicators score was calculated using the formula \(\left(achieved \,in \%=\frac{indicator \,score \,x \,100}{indicator\, weight} \right)\) [ 26 , 28 ].

The independent variables for the acceptability dimension were socio-demographic and economic variables (age, educational status, marital status, occupation of caregiver, family size, income level, and mode of transport), availability of prescribed drugs, waiting time, travel time to ICCM site, home to home visit, consultation time, appointment, and source of information.

The overall implementation of ICCM was measured by using 49 indicators over the three dimensions: availability (17 indicators), compliance (18 indicators) and acceptability (14 indicators).

Program logic model

Based on the constructed program logic model and trained health care providers, mothers/caregivers received health information and counseling on child feeding; children were assessed, classified, and treated for disease, received follow-up; they were checked for vitamin A; and deworming and immunization status were the expected outputs of the program activities. Improved knowledge of HEWs on ICCM, increased health-seeking behavior, improved quality of health services, increased utilization of services, improved data quality and information use, and improved child health conditions are considered outcomes of the program. Reduction of under-five morbidity and mortality and improving quality of life in the society are the distant outcomes or impacts of the program (Fig.  1 ).

Integrated community case management of childhood illness program logic model in Gondar City in 2022

Data collection tools and procedure

Resource inventory and data extraction checklists were adapted from standard ICCM tool and check lists [ 29 ]. A structured interviewer administered questionnaire was adapted by referring different literatures [ 30 , 31 ] to measure the acceptability of ICCM. The key informant interview (KII) guide was also developed to explore the views of KIs. The interview questionnaire and guide were initially developed in English and translated into the local language (Amharic) and finally back to English to ensure consistency. All the interviews were done in the local language, Amharic.

Five trained clinical nurses and one BSC nurse were recruited from Gondar zuria and Wegera district as data collectors and supervisors, respectively. Two days training on the overall purpose of the evaluation and basic data collection procedures were provided prior to data collection. Then, both quantitative and qualitative data were gathered at the same time. The quantitative data were gathered from program documentation, charts of ICCM program visitors and, exit interview. Interviews with 21 KIIs and non-participatory observations of patient-provider interactions were used to acquire qualitative data. Key informant interviews were conducted to investigate the gaps and best practices in the implementation of the ICCM program.

A pretest was conducted to 26 mothers/caregivers/ at Maksegnit health post and appropriate modifications were made based on the pretest results. The data collectors were supervised and principal evaluator examined the completeness and consistency of the data on a daily basis.

Data management and analysis

For analysis, quantitative data were entered into epi-data version 4.6 and exported to Stata 14 software for analysis. Narration and tabular statistics were used to present descriptive statistics. Based on established judgment criteria, the total program implementation was examined and interpreted as a mix of the availability, compliance, and acceptability dimensions. To investigate the factors associated with ICCM acceptance, a binary logistic regression analysis was performed. During bivariable analysis, variables with p-values less than 0.25 were included in multivariable analysis. Finally, variables having a p-value less than 0.05 and an adjusted odds ratio (AOR) with a 95% confidence interval (CI) were judged statistically significant. Qualitative data were collected recorded, transcribed into Amharic, then translated into English and finally coded and thematically analyzed.

Judgment matrix analysis

The weighted values of availability, compliance, and acceptability dimensions were 35, 40, and 25 based on the stakeholder and investigator agreement on each indicator, respectively. The judgment parameters for each dimension and the overall implementation of the program were categorized as poor (< 60%), fair (60–74.9%), good (75-84.9%), and very good (85–100%).

Availability of resources

A total of 26 HEWs were assigned within the fourteen health posts, and 72.7% of them were trained on ICCM to manage common childhood illnesses in under-five children. However, the training was given before four years, and they didn’t get even refreshment training about ICCM. The KII responses also supported that the shortage of HEWs at the HPs was the problem in implementing the program properly.

I am the only HEW in this health post and I have not been trained on ICCM program. So, this may compromise the quality of service and client satisfaction.(25 years old HEW with two years’ experience)

All observed health posts had ICCM registration books, monthly report and referral formats, functional thermometer, weighting scale and MUAC tape meter. However, timer and resuscitation bag was not available in all HPs. Most of the key informant finding showed that, in all HPs there was no shortage of guideline, registration book and recording tool; however, there was no OTP card in some health posts.

“Guideline, ICCM registration book for 2–59 months of age, and other different recording and reporting formats and booklet charts are available since September/2016. However, OTP card is not available in most HPs.”. (A 30 years male health center director)

Only one-fifth (21%) of HPs had a clean water source for drinking and washing of equipment. Most of Key-informant interview findings showed that the availability of infrastructures like water was not available in most HPs. Poor linkage between HPs, HCs, town health department, and local Kebele administer were the reason for unavailability.

Since there is no water for hand washing, or drinking, we obligated to bring water from our home for daily consumptions. This increases the burden for us in our daily activity. (35 years old HEW)

Most medicines, such as anti-malaria drugs with RDT, Quartem, Albendazole, Amoxicillin, vitamin A capsules, ORS, and gloves, were available in all the health posts. Drugs like zinc, paracetamol, TTC eye ointment, and folic acid were available in some HPs. However, cotrimoxazole and vitamin K capsules were stocked-out in all health posts for the last six months. The key informant also revealed that: “Vitamin K was not available starting from the beginning of this program and Cotrimoxazole was not available for the past one year and they told us they would avail it soon but still not availed. Some essential ICCM drugs like anti malaria drugs, De-worming, Amoxicillin, vitamin A capsules, ORS and medical supplies were also not available in HCs regularly.”(28 years’ Female PHCU focal)

The overall availability of resources for ICCM implementation was 84.2% which was good based on our presetting judgment parameter (Table  1 ).

Health extension worker’s compliance

From the 42 patient-provider interactions, we found that 85.7%, 71.4%, 76.2%, and 95.2% of the children were checked for body temperature, weight, general danger signs, and immunization status respectively. Out of total (42) observation, 33(78.6%) of sick children were classified for their nutritional status. During observation time 29 (69.1%) of caregivers were counseled by HEWs on food, fluid and when to return back and 35 (83.3%) of children were appointed for next follow-up visit. Key informant interviews also affirmed that;

“Most of our health extension workers were trained on ICCM program guidelines but still there are problems on assessment classification and treatment of disease based on guidelines and standards this is mainly due to lack refreshment training on the program and lack of continuous supportive supervision from the respective body.” (27years’ Male health center head)

From 10 clients classified as having severe pneumonia cases, all of them were referred to a health center (with pre-referral treatment), and from those 57 pneumonia cases, 50 (87.7%) were treated at the HP with amoxicillin or cotrimoxazole. All children with severe diarrhea, very severe disease, and severe complicated malnutrition cases were referred to health centers with a pre-referral treatment for severe dehydration, very severe febrile disease, and severe complicated malnutrition, respectively. From those with some dehydration and no dehydration cases, (82.4%) and (86.8%) were treated at the HPs for some dehydration (ORS; plan B) and for no dehydration (ORS; plan A), respectively. Moreover, zinc sulfate was prescribed for 63 (90%) of under-five children with some dehydration or no dehydration. From 26 malaria cases and 32 severe uncomplicated malnutrition and moderate acute malnutrition cases, 20 (76.9%) and 25 (78.1%) were treated at the HPs, respectively. Of the total reviewed documents, 56 (93.3%), 66 (94.3%), 38 (84.4%), and 25 (78.1%) of them were given a follow-up date for pneumonia, diarrhea, malaria, and malnutrition, respectively.

Supportive supervision and performance review meetings were conducted only in 10 (71.4%) HPs, but all (100%) HPs sent timely reports to the next supervisory body.

Most of the key informants’ interview findings showed that supportive supervision was not conducted regularly and for all HPs.

I had mentored and supervised by supportive supervision teams who came to our health post at different times from health center, town health office and zonal health department. I received this integrated supervision from town health office irregularly, but every month from catchment health center and last integrated supportive supervision from HC was on January. The problem is the supervision was conducted for all programs.(32 years’ old and nine years experienced female HEW)

Moreover, the result showed that there was poor compliance of HEWs for the program mainly due to weak supportive supervision system of managerial and technical health workers. It was also supported by key informants as:

We conducted supportive supervision and performance review meeting at different time, but still there was not regular and not addressed all HPs. In addition to this the supervision and review meeting was conducted as integration of ICCM program with other services. The other problem is that most of the time we didn’t used checklist during supportive supervision. (Mid 30 years old male HC director)

Based on our observation and ICCM document review, 83.1% of the HEWs were complied with the ICCM guidelines and judged as fair (Table  2 ).

Acceptability of ICCM program

Sociodemographic and obstetric characteristics of participants.

A total of 484 study participants responded to the interviewer-administered questionnaire with a response rate of 95.3%. The mean age of study participants was 30.7 (SD ± 5.5) years. Of the total caregivers, the majority (38.6%) were categorized under the age group of 26–30 years. Among the total respondents, 89.3% were married, and regarding religion, the majorities (84.5%) were Orthodox Christian followers. Regarding educational status, over half of caregivers (52.1%) were illiterate (unable to read or write). Nearly two-thirds of the caregivers (62.6%) were housewives (Table  3 ).

All the caregivers came to the health post on foot, and most of them 418 (86.4%) arrived within one hour. The majority of 452 (93.4%) caregivers responded that the waiting time to get the service was less than 30 min. Caregivers who got the prescribed drugs at the health post were 409 (84.5%). Most of the respondents, 429 (88.6%) and 438 (90.5%), received counseling services on providing extra fluid and feeding for their sick child and were given a follow-up date.

Most 298 (61.6%) of the caregivers were satisfied with the convenience of the working hours of HPs, and more than three-fourths (80.8%) were satisfied with the counseling services they received. Most of the respondents, 366 (75.6%), were satisfied with the appropriateness of waiting time and 431 (89%) with the appropriateness of consultation time. The majority (448 (92.6%) of caregivers were satisfied with the way of communicating with HEWs, and 269 (55.6%) were satisfied with the knowledge and competence of HEWs. Nearly half of the caregivers (240, or 49.6%) were satisfied with the availability of drugs at health posts.

The overall acceptability of the ICCM program was 75.3%, which was judged as good. A low proportion of acceptability was measured on the cleanliness of the health posts, the appropriateness of the waiting area, and the competence and knowledge of the HEWs. On the other hand, high proportion of acceptability was measured on appropriateness of waiting time, way of communication with HEWs, and the availability of drugs (Table  4 ).

Factors associated with acceptability of ICCM program

In the final multivariable logistic regression analysis, educational status of caregivers, availability of prescribed drugs, time to arrive, and waiting time were factors significantly associated with the satisfaction of caregivers with the ICCM program.

Accordingly, the odds of caregivers with primary education, secondary education, and college and above were 73% (AOR = 0.27, 95% CI: 0.11–0.52), 84% (AOR = 0.16, 95% CI: 0.07–0.39), and 92% (AOR = 0.08, 95% CI: 0.07–0.40) less likely to accept the program as compared to mothers or caregivers who were not able to read and write, respectively. The odds of caregivers or mothers who received prescribed drugs were 2.17 times more likely to accept the program as compared to their counters (AOR = 2.17, 95% CI: 1.14–4.10). The odds of caregivers or mothers who waited for services for less than 30 min were 2.8 times more likely to accept the program as compared to those who waited for more than 30 min (AOR = 2.80, 95% CI: 1.16–6.79). Moreover, the odds of caregivers/mothers who traveled an hour or less for service were 3.8 times more likely to accept the ICCM program as compared to their counters (AOR = 3.82, 95% CI:1.99–7.35) (Table  5 ).

Overall ICCM program implementation and judgment

The implementation of the ICCM program in Gondar city administration was measured in terms of availability (84.2%), compliance (83.1%), and acceptability (75.3%) dimensions. In the availability dimension, amoxicillin, antimalarial drugs, albendazole, Vit. A, and ORS were available in all health posts, but only six HPs had Ready-to-Use Therapeutic Feedings, three HPs had ORT Corners, and none of the HPs had functional timers. In all health posts, the health extension workers asked the chief to complain, correctly assessed for pneumonia, diarrhea, malaria, and malnutrition, and sent reports based on the national schedule. However, only 70% of caretakers counseled about food, fluids, and when to return, 66% and 76% of the sick children were checked for anemia and other danger signs, respectively. The acceptability level of the program by caretakers and caretakers’/mothers’ educational status, waiting time to get the service and travel time ICCM sites were the factors affecting its acceptability. The overall ICCM program in Gondar city administration was 81.5% and judged as good (Fig.  2 ).

Overall ICCM program implementation and the evaluation dimensions in Gondar city administration, 2022

The implementation status of ICCM was judged by using three dimensions including availability, compliance and acceptability of the program. The judgment cut of points was determined during evaluability assessment (EA) along with the stakeholders. As a result, we found that the overall implementation status of ICCM program was good as per the presetting judgment parameter. Availability of resources for the program implementation, compliance of HEWs to the treatment guideline and acceptability of the program services by users were also judged as good as per the judgment parameter.

This evaluation showed that most medications, equipment and recording and reporting materials available. This finding was comparable with the standard ICCM treatment guide line [ 10 ]. On the other hand trained health care providers, some medications like Zink, Paracetamol and TTC eye ointment, folic acid and syringes were not found in some HPs. However the finding was higher than the study conducted in SNNPR on selected health posts [ 33 ] and a study conducted in Soro district, southern Ethiopia [ 24 ]. The possible reason might be due to low interruption of drugs at town health office or regional health department stores, regular supplies of essential drugs and good supply management and distribution of drug from health centers to health post.

The result of this evaluation showed that only one fourth of health posts had functional ORT Corner which was lower compared to the study conducted in SNNPR [ 34 ]. This might be due poor coverage of functional pipe water in the kebeles and the installation was not set at the beginning of health post construction as reported from one of ICCM program coordinator.

Compliance of HEWs to the treatment guidelines in this evaluation was higher than the study done in southern Ethiopia (65.6%) [ 24 ]. This might be due to availability of essential drugs educational level of HEWs and good utilization of ICCM guideline and chart booklet by HEWs. The observations showed most of the sick children were assessed for danger sign, weight, and temperature respectively. This finding is lower than the study conducted in Rwanda [ 35 ]. This difference might be due to lack of refreshment training and regular supportive supervision for HEWs. This also higher compared to the study done in three regions of Ethiopia indicates that 88%, 92% and 93% of children classified as per standard for Pneumonia, diarrhea and malaria respectively [ 36 ]. The reason for this difference may be due to the presence of medical equipment and supplies including RDT kit for malaria, and good educational level of HEWs.

Moreover most HPs received supportive supervision and performance review meeting was conducted and all of them send reports timely to next level. The finding of this evaluation was lower than the study conducted on implementation evaluation of ICCM program southern Ethiopia [ 24 ] and study done in three regions of Ethiopia (Amhara, Tigray and SNNPR) [ 37 ]. This difference might be due sample size variation.

The overall acceptability of the ICCM program was less than the presetting judgment parameter but slightly higher compared to the study in southern Ethiopia [ 24 ]. This might be due to presence of essential drugs for treating children, reasonable waiting and counseling time provided by HEWs, and smooth communication between HEWs and caregivers. In contrast, this was lower than similar studies conducted in Wakiso district, Uganda [ 38 ]. The reason for this might be due to contextual difference between the two countries, inappropriate waiting area to receive the service and poor cleanness of the HPs in our study area. Low acceptability of caregivers to ICCM service was observed in the appropriateness of waiting area, availability of drugs, cleanness of health post, and competence of HEWs while high level of caregiver’s acceptability was consultation time, counseling service they received, communication with HEWs, treatment given for their sick children and interest to return back for ICCM service.

Caregivers who achieved primary, secondary, and college and above were more likely accept the program services than those who were illiterate. This may more educated mothers know about their child health condition and expect quality service from healthcare providers which is more likely reduce the acceptability of the service. The finding is congruent with a study done on implementation evaluation of ICCM program in southern Ethiopia [ 24 ]. However, inconsistent with a study conducted in wakiso district in Uganda [ 38 ]. The possible reason for this might be due to contextual differences between the two countries. The ICCM program acceptability was high in caregivers who received all prescribed drugs than those did not. Caregivers those waited less than 30 min for service were more accepted ICCM services compared to those more than 30 minutes’ waiting time. This finding is similar compared with the study conducted on implementation evaluation of ICCM program in southern Ethiopia [ 24 ]. In contrary, the result was incongruent with a survey result conducted by Ethiopian public health institute in all regions and two administrative cities of Ethiopia [ 39 ]. This variation might be due to smaller sample size in our study the previous one. Moreover, caregivers who traveled to HPs less than 60 min were more likely accepted the program than who traveled more and the finding was similar with the study finding in Jimma zone [ 40 ].

Strengths and limitations

This evaluation used three evaluation dimensions, mixed method and different data sources that would enhance the reliability and credibility of the findings. However, the study might have limitations like social desirability bias, recall bias and Hawthorne effect.

The implementation of the ICCM program in Gondar city administration was measured in terms of availability (84.2%), compliance (83.1%), and acceptability (75.3%) dimensions. In the availability dimension, amoxicillin, antimalarial drugs, albendazole, Vit. A, and ORS were available in all health posts, but only six HPs had Ready-to-Use Therapeutic Feedings, three HPs had ORT Corners, and none of the HPs had functional timers.

This evaluation assessed the implementation status of the ICCM program, focusing mainly on availability, compliance, and acceptability dimensions. The overall implementation status of the program was judged as good. The availability dimension is compromised due to stock-outs of chloroquine syrup, cotrimoxazole, and vitamin K and the inaccessibility of clean water supply in some health posts. Educational statuses of caregivers, availability of prescribed drugs at the HPs, time to arrive to HPs, and waiting time to receive the service were the factors associated with the acceptability of the ICCM program.

Therefore, continuous supportive supervision for health facilities, and refreshment training for HEW’s to maximize compliance are recommended. Materials and supplies shall be delivered directly to the health centers or health posts to solve the transportation problem. HEWs shall document the assessment findings and the services provided using the registration format to identify their gaps, limitations, and better performances. The health facilities and local administrations should construct clean water sources for health facilities. Furthermore, we recommend for future researchers and program evaluators to conduct longitudinal studies to know the causal relationship of the program interventions and the outcomes.

Data availability

Data will be available upon reasonable request from the corresponding author.

Abbreviations

Ethiopian Demographic and Health Survey

Health Center/Health Facility

Health Extension Program

Health Extension Workers

Health Post

Health Sector Development Plan

Integrated Community Case Management of Common Childhood Illnesses

Information Communication and Education

Integrated Family Health Program

Integrated Management of Neonatal and Childhood Illness

Integrated Supportive Supervision

Maternal and Child Health

Mid Upper Arm Circumference

Non-Government Organization

Oral Rehydration Salts

Outpatient Therapeutic program

Primary health care unit

Rapid Diagnostics Test

Ready to Use Therapeutic Foods

Sever Acute Malnutrition

South Nation Nationalities People Region

United Nations International Child Emergency Fund

World Health Organization

Brenner JL, Barigye C, Maling S, Kabakyenga J, Nettel-Aguirre A, Buchner D, et al. Where there is no doctor: can volunteer community health workers in rural Uganda provide integrated community case management? Afr Health Sci. 2017;17(1):237–46.

Article   PubMed   PubMed Central   Google Scholar  

Mubiru D, Byabasheija R, Bwanika JB, Meier JE, Magumba G, Kaggwa FM, et al. Evaluation of integrated community case management in eight districts of Central Uganda. PLoS ONE. 2015;10(8):e0134767.

Samuel S, Arba A. Utilization of integrated community case management service and associated factors among mothers/caregivers who have sick eligible children in southern Ethiopia. Risk Manage Healthc Policy. 2021;14:431.

Article   Google Scholar  

Kavle JA, Pacqué M, Dalglish S, Mbombeshayi E, Anzolo J, Mirindi J, et al. Strengthening nutrition services within integrated community case management (iCCM) of childhood illnesses in the Democratic Republic of Congo: evidence to guide implementation. Matern Child Nutr. 2019;15:e12725.

Miller NP, Amouzou A, Tafesse M, Hazel E, Legesse H, Degefie T, et al. Integrated community case management of childhood illness in Ethiopia: implementation strength and quality of care. Am J Trop Med Hyg. 2014;91(2):424.

WHO. Annual report 2016: Partnership and policy engagement. World Health Organization, 2017.

Banteyerga H. Ethiopia’s health extension program: improving health through community involvement. MEDICC Rev. 2011;13:46–9.

Article   PubMed   Google Scholar  

Wang H, Tesfaye R, Ramana NV, Chekagn G. CT. Ethiopia health extension program: an institutionalized community approach for universal health coverage. The World Bank; 2016.

Donnelly J. Ethiopia gears up for more major health reforms. Lancet. 2011;377(9781):1907–8.

Legesse H, Degefie T, Hiluf M, Sime K, Tesfaye C, Abebe H, et al. National scale-up of integrated community case management in rural Ethiopia: implementation and early lessons learned. Ethiop Med J. 2014;52(Suppl 3):15–26.

Google Scholar  

Miller NP, Amouzou A, Hazel E, Legesse H, Degefie T, Tafesse M et al. Assessment of the impact of quality improvement interventions on the quality of sick child care provided by Health Extension workers in Ethiopia. J Global Health. 2016;6(2).

Oliver K, Young M, Oliphant N, Diaz T, Kim JJNYU. Review of systematic challenges to the scale-up of integrated community case management. Emerging lessons & recommendations from the catalytic initiative (CI/IHSS); 2012.

FMoH E. Health Sector Transformation Plan 2015: https://www.slideshare.net . Accessed 12 Jan 2022.

McGorman L, Marsh DR, Guenther T, Gilroy K, Barat LM, Hammamy D, et al. A health systems approach to integrated community case management of childhood illness: methods and tools. The American Journal of Tropical Medicine and Hygiene. 2012;87(5 Suppl):69.

Young M, Wolfheim C, Marsh DR, Hammamy D. World Health Organization/United Nations Children’s Fund joint statement on integrated community case management: an equity-focused strategy to improve access to essential treatment services for children. The American journal of tropical medicine and hygiene. 2012;87(5 Suppl):6.

Ezbakhe F, Pérez-Foguet A. Child mortality levels and trends. Demographic Research.2020;43:1263-96.

UNICEF, Ending child deaths from pneumonia and diarrhoea. 2016 report: Available at https://data.unicef.org. accessed 13 Jan 2022.

UNITED NATIONS, The Millinium Development Goals Report 2015: Available at https://www.un.org.Accessed 12 Jan 2022

Bent W, Beyene W, Adamu A. Factors Affecting Implementation of Integrated Community Case Management Of Childhood Illness In South West Shoa Zone, Central Ethiopia 2015.

Abdosh B. The quality of hospital services in eastern Ethiopia: Patient’s perspective.The Ethiopian Journal of Health Development. 2006;20(3).

Young M, Wolfheim C, Marsh DR, Hammamy DJTAjotm, hygiene. World Health Organization/United Nations Children’s Fund joint statement on integrated community case management: an equity-focused strategy to improve access to essential treatment services for children.2012;87(5_Suppl):6–10.

Obrist B, Iteba N, Lengeler C, Makemba A, Mshana C, Nathan R, et al. Access to health care in contexts of livelihood insecurity: a framework for analysis and action.PLoS medicine. 2007;4(10):e308.

Carroll C, Patterson M, Wood S, Booth A, Rick J, Balain S. A conceptual framework for implementation fidelity. Implementation science. 2007;2(1):1–9.

Dunalo S, Tadesse B, Abraham G. Implementation Evaluation of Integrated Community Case Management of Common Childhood Illness (ICCM) Program in Soro Woreda, Hadiya Zone Southern Ethiopia 2017 2017.

Asefa G, Atnafu A, Dellie E, Gebremedhin T, Aschalew AY, Tsehay CT. Health System Responsiveness for HIV/AIDS Treatment and Care Services in Shewarobit, North Shewa Zone, Ethiopia. Patient preference and adherence. 2021;15:581.

Gebremedhin T, Daka DW, Alemayehu YK, Yitbarek K, Debie A. Process evaluation of the community-based newborn care program implementation in Geze Gofa district,south Ethiopia: a case study evaluation design. BMC pregnancy and childbirth. 2019;19(1):1–13.

Pitaloka DS, Rizal A. Patient’s satisfaction in antenatal clinic hospital Universiti Kebangsaan Malaysia. Jurnal Kesihatan Masyarakat (Malaysia). 2006;12(1):1–10.

Teshale G, Debie A, Dellie E, Gebremedhin T. Evaluation of the outpatient therapeutic program for severe acute malnourished children aged 6–59 months implementation in Dehana District, Northern Ethiopia: a mixed-methods evaluation. BMC pediatrics. 2022;22(1):1–13.

Mason E. WHO’s strategy on Integrated Management of Childhood Illness. Bulletin of the World Health Organization. 2006;84(8):595.

Shaw B, Amouzou A, Miller NP, Tafesse M, Bryce J, Surkan PJ. Access to integrated community case management of childhood illnesses services in rural Ethiopia: a qualitative study of the perspectives and experiences of caregivers. Health policy and planning.2016;31(5):656 – 66.

Organization WH. Annual report 2016: Partnership and policy engagement. World Health Organization, 2017.

Berhanu D, Avan B. Community Based Newborn Care Baseline Survey Report Ethiopia,October 2014.

Save the children, Enhancing Ethiopia’s Health Extension Package in the Southern Nations and Nationalities People’s Region (SNNPR) Shebedino and Lanfero Woredas report.Hawassa;. 2012: Avalable at https://ethiopia.savethechildren.net

Kolbe AR, Muggah R, Hutson RA, James L, Puccio M, Trzcinski E, et al. Assessing Needs After the Quake: Preliminary Findings from a Randomized Survey of Port-au-Prince Households. University of Michigan/Small Arms Survey: Available at https://deepbluelibumichedu PDF. 2010.

Teferi E, Teno D, Ali I, Alemu H, Bulto T. Quality and use of IMNCI services at health center under-five clinics after introduction of integrated community-based case management (ICCM) in three regions of Ethiopia. Ethiopian Medical Journal. 2014;52(Suppl 3):91 – 8.

Last 10 Km project, Integrated Community Case Management (iCCM) Survey report in Amhara, SNNP, and Tigray Regions, 2017: Avaialable at https://l10k.jsi.com

Tumuhamye N, Rutebemberwa E, Kwesiga D, Bagonza J, Mukose A. Client satisfaction with integrated community case management program in Wakiso District, Uganda, October 2012: A cross sectional survey. Health scrip org. 2013;2013.

EPHI. Ethiopia service provision assessment plus survey 2014 report: available at http://repository.iifphc.org

Gintamo B. EY, Assefa Y. Implementation Evaluation of IMNCI Program at Public Health Centers of Soro District, Hadiya Zone, Southern Ethiopia,. 2017: Available at https://repository.ju.edu.et

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Acknowledgements

We are very grateful to University of Gondar and Gondar town health office for its welcoming approaches. We would also like to thank all of the study participants of this evaluation for their information and commitment. Our appreciation also goes to the data collectors and supervisors for their unreserved contribution.

No funding is secured for this evaluation study.

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Metema District Health office, Gondar, Ethiopia

Mekides Geta

Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, P.O. Box 196, Gondar, Ethiopia

Geta Asrade Alemayehu, Wubshet Debebe Negash, Tadele Biresaw Belachew, Chalie Tadie Tsehay & Getachew Teshale

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Contributions

All authors contributed to the preparation of the manuscript. M.G. conceived and designed the evaluation and performed the analysis then T.B.B., W.D.N., G.A.A., C.T.T. and G.T. revised the analysis. G.T. prepared the manuscript and all the authors revised and approved the final manuscript.

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Correspondence to Getachew Teshale .

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Ethical approval was obtained from Institutional Review Board (IRB) of Institute of Public Health, College of Medicine and Health sciences, University of Gondar (Ref No/IPH/1482/2013). Informed consent was obtained from all subjects and/or their legal guardian(s).

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All authors declared that they have no competing interest.

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Geta, M., Alemayehu, G.A., Negash, W.D. et al. Evaluation of integrated community case management of the common childhood illness program in Gondar city, northwest Ethiopia: a case study evaluation design. BMC Pediatr 24 , 310 (2024). https://doi.org/10.1186/s12887-024-04785-0

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Received : 20 February 2024

Accepted : 22 April 2024

Published : 09 May 2024

DOI : https://doi.org/10.1186/s12887-024-04785-0

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