Characterizing a Rational Healthcare Value Network

A worth system is an advertising idea that depicts the social and specialized assets (inventory network) inside and between organizations. They represent the general worth of items and administrations, including the accumulation of upstream suppliers, downstream channels to market, and subordinate administrations that help a typical plan of action inside an industry. The sort of worth system that the medicinal services framework needs concentrates on bringing expanded quality to health awareness tolerant (buyer) and prize suppliers for conveying high-esteem mind.

Healthcare Value Network

Such a worth system has four essential cooperating parts, as portrayed in figure above. These parts empower cyclical information streams influenced by drivers and obstructions that expand consideration quality to the patient:

The green box alludes to three sorts of information needed to construct the data and learning individuals requirement for expanding esteem over the inventory network. The instruction information alludes to formal and casual ways that individuals impart their insight, thoughts, and encounters. Research information, then again, is utilized as a part of controlled clinical trials, results and execution studies, different sorts of biosurveillance (e.g., post-business sector medication and gadget, open wellbeing), favored clinical rule improvement, and different sorts of examination. Engineering information alludes to the date gathered by Ehrs and other wellbeing IT instruments, and streamed through solid therapeutic gear.

The five blue boxes allude the change of the information utilizing a mixture of HIT devices and clinical techniques that advertise the sorts of learning and comprehension that encourages more successful and effective consideration (administrations and items). They incorporate (an) utilization of proof based rules, customized forethought plans, choice help devices, and correspondence systems; (b) routines for data imparting and consideration coordination; and (c) understanding strengthening. Every movement (process) in the blue boxes backings esteem by obliging quality handoffs and nonstop estimation, appraisal, input and acknowledgement at every breakpoint (the hole between every action) to guarantee esteem creation through consistent quality change (CQI).

The red box on the bottom alludes to the mechanical, mental, monetary, and administrative elements that elevate or repress worth to patient by impacting (driving or blocking) the blue box forms. A portion of the key impacts are recorded in the case.

The purple box speaks to great patient conclusions; it is the sought aftereffect of utilizing the information, devices and methods to expand worth to the patient. The bended purple shaft indicating the Data Types box demonstrates the need to give information about the procedure, impacts and results of forethought over the whole inventory network through persistent input circles. The blue box exercises and their related impacts that help accomplish the objective of higher quality at lower expense are strengthened; those that don't are adjusted or wiped out.

How to Cure Health Care

Since the end of World War II, the provision of medical care in the United States and other advanced countries has displayed three major features: first, rapid advances in the science of medicine; second, large increases in spending, both in terms of inflation-adjusted dollars per person and the fraction of national income spent on medical care; and third, rising dissatisfaction with the delivery of medical care, on the part of both consumers of medical care and physicians and other suppliers of medical care.

Rapid technological advances have occurred repeatedly since the Industrial Revolution—in agriculture, steam engines, railroads, telephones, electricity, automobiles, radio, television, and, most recently, computers and telecommunication. The other two features seem unique to medicine. It is true that spending initially increased after non medical technical advances, but the fraction of national income spent did not increase dramatically after the initial phase of widespread acceptance. 

On the contrary, technological development lowered cost, so that the fraction of national income spent on food, transportation, communication, and much more has gone down, releasing resources to produce new products or services. Similarly, there seems no counterpart in these other areas to the rising dissatisfaction with the delivery of medical care.

Data->Information->Knowledge: Formula for improving healthcare

As a clinician, health IT architect and computational model-builder, I’ve been focused for the past three decades on how to use health IT to transform data into information and information into knowledge, in a way that improve care value. I’ve come to realize that highly effective and efficient care delivery (including prevention, assessment of risk, and the diagnosis oand treatment of health problems) depends on useful, valid clinical knowledge providing evidence-based decision support.

This knowledge can help continually improve care outcomes though methods and tools such as patient-centeredcognitive support, computerized clinical decision systems, and evidence-based clinical practice guidelines/pathways. These things are necessary if we want bridge the knowledgegap.
In any case, gaining this crucial knowledge depends on creating, continually evolving and disseminating useful, actionable, valid information and presenting it in a way that avoids overloading theclinician and patient.

And generating such valuable information requires adequate amounts and diversities of valid and reliable data. Some of these requisite data can come from today’s "Big Data" stores, which are typically insurance claims (administrative) data. While such claims data have usefulness, they are grossly inadequate when it comes to creating the kinds of information and emerging the kinds of clinical knowledge necessary to improve care quality and cost in any truly meaningful way.

Playing with models in loosely coupled social networks

Playing with models in loosely coupled social networks
In this post, I present two concepts which, when combined, have the potential to transform our healthcare system in profoundly positive ways.

The first is “loosely-coupled social networks” in which people from multiple locations and with different roles, responsibilities and experiences work together to make decisions beyond the knowledge or skills of any individual. Collaboration among people with wide diversities of knowledge, ideas and points of view provides a larger collection of intellectual resource, and offers access to a greater variety of non-redundant information and knowledge on which to base decisions. Compare this to a tightly-coupled network that limits participation to people within the same discipline, department, region, etc. and with people who have access to the same information sources and who share similar experiences. In the loosely-coupled social networks are the greatest opportunities for stimulating multifaceted discussions, out-of-the box thinking, and creative solutions.

The second concept is “sharing & playing with models.” There are many different types of models used in healthcare, including models for defining health problems/diagnoses (e.g., ICD and DSM codes) and treatments (e.g., CPT and ABC codes), for assessing and managing clinical and financial issues/risk (e.g., retrospective encounter and claims data analyses), for evaluating performance (e.g., variance analysis and risk-adjustment), for deciding the interventions to render and procedures to follow (e.g., clinical guidelines and pathways), for testing hypotheses and assumptions, for paying for care (e.g., HSA/HDHP and traditional indemnity insurance), for rationing care (e.g., QALY), and so on. When people share and play with models, they compare models and test them for their ability to reflect reality accurately; they manipulate the models to represent different scenarios, such as “what if” scenarios about the probability of future occurrences; and they discuss the assumptions and results the models produce. When they find models that disagree or generate invalid results, they examine the fundamental assumptions built into the models, looking for logical flaws and inconsistencies, questioning the authors' perception of reality, and debating about the assumptions and practical value of the model. By challenging their assumptions, useful counterintuitive insights often emerge, innovative thought is sparked, new questions arise, relationships are developed, the influence of an organization’s culture and politics are revealed, and compelling and unexpected management issues are discovered. This means that sharing and playing with models is an effective path to innovation, risk management, and value creation.

Conclusion: By encouraging people in loosely coupled social networks to share and play with models, radical innovation is fostered by disrupting of status quo, which enables the models upon which decisions are made to evolve continuously. The bottom line is that connecting diverse groups of people and giving them the ability to model-play would produce continually improving models; and using these models to support decision would result in safer, higher quality, more cost-effective care.