Jan 132016
 

K15968-v2As we move into 2016 it is time to reflect on knowledge management and look at the future of this discipline. In my latest book: Knowledge Management in Practice I address what I believe is the future of KM in 2016 and beyond. An excerpt from chapter 18 follows.

Future of Knowledge Management:

One of the major areas in which KM will make an impact is within the customer service industry. Customer Service is the area in which most customers will have their only connection and interaction with your organization. It is this area where customers will form their opinions about the organization and determine if they remain a customer or move on to another competitor. Due to this scenario (and others) organizations invest a major portion of their revenue and attention to improving their customer service.

In an August 2014 Harvard Business Review article by Peter Kriss entitled “The Value of Customer Experience, Quantified” he states “Intuitively, most people recognize the value of a great customer experience. Brands that deliver them are ones that we want to interact with as customers that we become loyal to, and that we recommend to our friends and family”. Also, he states that the “value of delivering such an experience is often a lot less clear, because it can be hard to quantify” Delivering consistent and concise knowledge to provide answers to customer inquiries in an efficient way leads to providing value to the customer and improving the overall customer experience.

In support of this trend of KM in customer service, Forrester’s “Top Trends For Customer Service in 2015”, author Kate Leggett points out in trend #4, knowledge management’s impact when she states “Knowledge Will Evolve From Dialog To Cognitive Engagement. Organizations will look at ways to reduce the manual overhead of traditional knowledge management for customer service. They will start to explore cognitive engagement solutions, interactive computing systems that use artificial intelligence to collect information, automatically build models of understanding and inference, and communicate in natural ways. These solutions have the potential to automate knowledge creation, empower agents with deeply personalized answers and intelligence, scale a company’s knowledge capability, and uncover new revenue streams by learning about customer needs.”

IBM Watson is playing a significant role in the evolution of applications that automate knowledge creation by providing deeply personalized answers and intelligence. This technology will not only effect customer service, but a multitude of industries with its capability to extract knowledge from Big Data sources. The IBM Watson ecosystem will provide deep content analytics and intensive scientific discovery that will lead to improve cognition contributing to an organization’s knowledge capabilities. This supports Kate Leggett’s research and points out that KM will continue to play a significant role in delivering knowledge and decision making capabilities to the customer service industry for the foreseeable future (2016 and beyond).

Global View of KM

In reviewing the 2015 Global Knowledge Management Observatory Report, authors David Griffiths, Abi Jenkins and Zoe Kingston-Griffiths state “The Knowledge Management function in many organizations is in a state of general decline”. This as they indicate is due to the following factors:

  • “Satisfaction in Knowledge Management’s contribution to strategic and operational objectives within organizations is often poor.”
  • “Knowledge Management lacks maturity and integration within the vast majority of organizations.”
  • “Knowledge Management continues to be predominantly seen as a technology-led function.”
  • “Satisfaction with technology-led Knowledge Management solutions is not improving.”
  • “Many Knowledge Management professionals do not appear to have the necessary awareness and/or permissions required to respond to unmet demand for KM activities in organizations.”
  • “Knowledge Management, as a field or area of practice, is argued to be suffering from a lack of specialist practitioners.”
  • “The value and/or significance of Knowledge Management activities is still not being appropriately recognized or reported within most organizations.”

Solutions that address many of the findings of the 2015 Global Knowledge Management Observatory Report are essential for KM success in 2016 and as KM evolves as a discipline. This includes producing a comprehensive KM strategy, KM education options, adopting KM programs, project and systems, and addressing why KM programs/projects fail.

All of these aspects from the 2015 Global KM Observatory Report are addressed in Knowledge Management in Practice. This book should be leveraged as a reference/guide and presents a tremendous resource to support the growth of KM at your organization.

Nov 302015
 

Chapter 9 - Figure 2 - Model for KM in Healthcare

Healthcare is a knowledge intensive business. Making the best use of knowledge within any healthcare provider organization (hospital, clinic, pharmacy, physician private practice, etc.) is essential for optimal patient care as well as cutting and/or streamlining costs. Knowledge Management (KM) in healthcare is about sharing know-how through collaboration and integration of systems to enable access to knowledge. Applying knowledge sharing and collaboration to healthcare would include sharing medical research, giving visibility to patient decisions, and collaboration between physicians and healthcare provider organizations. Collaborative work environments will bring more effective communication and more physician responsiveness to patients.

Healthcare is also a massive industry, and every healthcare provider organization faces challenges where incorporating knowledge management would be beneficial. The processes and systems that enable the delivery and management of healthcare services to patients are faced with the prospect of failing to prevent (and can indirectly or directly cause) suffering and in some cases death to the various patients it serves. It is for this reason that knowledge management is attracting much attention from the industry as a whole. However, it is now time that the healthcare industry start to implement KM and begin realizing the benefits that it can provide.

KM is a particularly complex issue for health organizations. The potential benefits knowledge-management implementation could bring are enormous. Some of these benefits include: better outcomes for patients, cost reduction, enhanced job flexibility, and improved responsiveness to patients’ needs and changing lifestyles and expectations and ensure more effective communication, leading to focused and (hopefully) seamless care interventions and a better patient experience.

In realizing these benefits it is understood that healthcare delivery is a knowledge driven process and KM provides the opportunity to incorporate knowledge management practices to improve the various healthcare processes.

To leverage knowledge management in the appropriate way to address the complex and process nature of healthcare, healthcare organizations should adopt a broad strategy to capture, communicate and apply explicit and tacit knowledge throughout the healthcare delivery process.

Healthcare Delivery Process

Delivery of healthcare is a complex endeavor. It includes primary organizations for healthcare delivery such as healthcare providers having inter-organizational relationships with other players (i.e., Blue Cross/Blue Shield and its member organizations, physician and hospital affiliations) to provide a foundation. The increasing cost of healthcare is putting pressure on access and quality of healthcare delivery and this is calling for increased accountability, because of high rates of medical errors, and globalization which leads to demands of higher standards of quality, are also putting pressures on healthcare delivery organizations.

The healthcare delivery process includes the following areas Patient Intake, Data Collection, Decision Support, Diagnosis and Treatment, and Patient Closeout. Each of these areas (depending on medical organization) have more complex processes, procedures and systems that enable them to integrate and function together. Below provides more details of each of the areas that comprise the healthcare delivery process:

Patient Intake Process: Due to the increase in patient demand partially caused by health reform initiatives that focus on broadening patient access to insurance, many medical practices are experiencing an increase in new patient enrollment. Whenever new patients use the services of a hospital or physician practice, they must complete forms that list their contact information, medical history, insurance information, and acknowledgement of various HIPAA regulations.

The patient intake process is the first opportunity to capture knowledge about the patient and his/her condition at the time of arrival at the healthcare facility. At this point the patient information is captured, along with method of payment, medical history and current vital condition. All of this data is transitioned to the facilities database. This presents an opportunity for the data to be shared, an opportunity for information to be processed from the data, and knowledge to be acquired from the information.

Data Collection: At this point of the process all the data that was taken during the intake process is collected and sent to the healthcare facilities’ database. The collection of healthcare data involves a diverse set of public and private data collection systems, including health surveys, administrative enrollment and billing records, and medical records, used by various entities, including hospitals, clinics, physicians, and health plans. This suggest the potential of each entity to contribute data, information and knowledge on patients or enrollees. As it stands now a fragmentation of data flow occurs because of these silos of data collection. One way to increase the flow of data, information and knowledge is to integrate them with data from other sources. However, it should be noted that a substantial fraction of the U.S. population does not have a regular relationship with a provider who integrates their care.

Decision Support System: This area of the healthcare delivery process involves integrating the Clinical Decision Support Systems (CDSS). The CDSS will enable the standardization and sharing of clinical best practices and protocols with staff, patients, and partners on demand, anywhere, and on any device. Physicians, nurses and other healthcare professionals use a CDSS to prepare a diagnosis and to review the diagnosis as a means of improving the final result. Data Mining (which will be examined later in this chapter) is conducted to examine the patient’s medical history in conjunction with relevant clinical research. Such analysis will provide the necessary knowledge to help predict potential events, which can range from drug interactions to disease symptoms. Some physicians may use a combination of a CDSS and their professional experience to determine the best course of care for a patient.

There are two main types of clinical decision support systems. One type of CDSS, which uses a knowledgebase (expert system), which applies rules to patient data using an inference engine and displays the results to the end user. Systems without a knowledge base, on the other hand, rely on machine learning to analyze clinical data. The challenge here is that a CDSS to be most effective it must be integrated with the healthcare organizations clinical workflow, which is often very complex. If a CDSS is a standalone system it will lack the interoperability needed to provide the knowledge necessary for healthcare professions to make a good determine of the best course of care for a patient.

However, the sheer number of clinical research and medical trials being published on an ongoing basis makes it difficult to incorporate the resulting data (Big Data). Additionally, incorporating Big Data into existing systems could causes a significant increase in infrastructure and maintenance.

Diagnosis and Treatment: Making a diagnosis is a very complex process, which includes cognitive tasks that involves both logical reasoning and pattern recognition. Although the process happens largely at an unconscious level, there are two essential steps where knowledge can be captured and applied.

In the first step, the healthcare professional will enumerate the diagnostic possibilities and estimate their relative likelihood. Experienced clinicians often group the findings into meaningful clusters and summarizes in brief phrases about the symptom, body location, or organ system involved.

In the second step in the diagnostic process, healthcare professional would incorporate new data, information and/or knowledge to change the relative probabilities, rule out some of the possibilities, and ultimately, choose the most likely diagnosis. For each diagnostic possibility, the additional knowledge increases or decreases its likelihood. At this point the diagnosis and treatment is rendered by the healthcare professional and the patient records are updated.

Patient Closeout/Patient Discharge: In the case of a simple patient closeout from a routine/scheduled physician visit or simple visit to the local clinic, the patient receives medication (if applicable), sets follow up appointments if necessary and finalized payment arrangements and the patient records are updated. However if you have had a hospital stay the discharge process can be quite involved. In the case of a discharge a set series of tasks must occur prior to discharging a patient. These tasks include examination and sign-off by appropriate providers and patient education. For each patient, the time of discharge and the tasks that need to be performed will be provided one day ahead of time. This allows for everyone involved in the discharge to self-organize on the day of discharge to get the work done within the window necessary to meet the scheduled discharge time (Institute for Health Improvement, 2015). At the conclusion of the discharge, patients receive information and instructions for continued care and follow up, in addition all patient records should be updated.

The following was an excerpt from my latest book: Knowledge Management in Practice

 

May 292015
 

TTFOver the next few months I will be posting excerpts from my latest book Knowledge Management in Practice. Today’s post is from Chapter 15 Drinking the KM Kool-Aid: Knowledge Management Adoption:

“Drinking the KM Kool-Aid” is a metaphor to indicate the adoption of Knowledge Management (program, policies, procedures and the methods and systems that enable it) throughout your organization…

Once the need for a KM Program has been determined and immediately after its official launch efforts must be on the way to initiate its adoption. In order to initiate the adoption of your knowledge Management program an effort to market the program and its various components has to be an intentional endeavor…

Utilizing the Task-Technology Fit (TTF) Model for Adoption of Knowledge Management Systems:

The TTF theory can be applied to examine the motivation of users to leverage a KM System to perform their organizational tasks and that applying TTF Theory to the KM Program can have a positive effect on the success and adoption of the KM Program.

In applying the TTF Theory to the KM Program a determination of where the TTF Theory fits within the KM Program structure must be identified. The TTF Theory holds that information technology is more likely to have a positive impact on individual and group performance if it is aligned with the tasks the users perform. When incorporating the KM System into the KM Program the system is aligned with the KM processes that have been identified that the KM Program will support. The KM processes of the KM Program reflect how workers within the organization use knowledge to perform their tasks. The TTF Theory is suited to measure this usage and can be leveraged to understand not only how the KM Systems are being used but will guide the KM Program administrators on the best way to increase adoption and contribute to increasing the performance of the workers who use the KM System.

TTF Theory is predicted to be a significant precursor to KMS usage. Furthermore the Task-Technology Fit (TTF) Theory has shown to be suitable for understanding the specific KM system needs of the program contributing and contribute to the KM Program Roadmap as it pertains to the alignment of technology to the specific milestones and objectives identified within the KM Program. This directly leads to understanding what the key aspects to adoption of the KM system are by the organization’s users and provides mechanisms to measure the rate of adoption contributing to measuring and improve the KM Program as it matures.

The examination of the TTF Theory and more about KM Adoption is detailed in chapter 15.