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

 

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