Reengineering acute episodic and chronic care delivery: the Geisinger Health System experience

2012 ◽  
Vol 33 (1) ◽  
pp. E16 ◽  
Author(s):  
Jonathan R. Slotkin ◽  
Alfred S. Casale ◽  
Glenn D. Steele ◽  
Steven A. Toms
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health System ◽  
Chronic Care ◽  
Health Care Policy ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Policy Research ◽  
Clinical Decision ◽  
Medical Decision

Comparative effectiveness research (CER) represents an evolution in clinical decision-making research that allows for the study of heterogeneous groups of patients with complex diseases processes. It has foundations in decision science, reliability science, and health care policy research. Health care finance will increasingly rely on CER for guidance in the coming years. There is increasing awareness of the importance of decreasing unwarranted variation in health care delivery. In the past 7 years, Geisinger Health System has performed broad reengineering of its acute episodic and chronic care delivery models utilizing macrosystem-level application of CER principles. These provider-driven process initiatives have resulted in significant improvement across all segments of care delivery, improved patient outcomes, and notable cost containment. These programs have led to the creation of novel pricing models, and when “hardwired” throughout a care delivery system, they can lead to correct medical decision making by 100% of providers in all patient encounters. Neurosurgery as a specialty faces unique challenges and opportunities with respect to broad adoption and application of CER techniques.

What Is Medical Ethics Consultation?

2008 ◽  
Vol 36 (1) ◽  
pp. 95-118 ◽  
Author(s):  
Giles R. Scofield
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Medical Ethics ◽  
Medical Technology ◽  
Clinical Decision Making ◽  
Ethics Consultation ◽  
Clinical Decision ◽  
Law Students ◽  
Medical Ethicist ◽  
Care Decision

As everybody knows, advances in medicine and medical technology have brought enormous benefits to, and created vexing choices for, us all – choices that can, and occasionally do, test the very limits of thinking itself. As everyone also knows, we live in the age of consultants, i.e., of professional experts who are ready, willing, and able to give us advice on any and every conceivable question. One such consultant is the medical ethics consultant, or the medical ethicist who consults.Medical ethics consultants involve themselves in just about every aspect of health care decision making. They help legislators and judges determine law, hospitals formulate policies, medical schools develop curricula, etc. In addition to educating physicians, nurses, and lawyers, amongst others, including medical, nursing, and law students, they participate in clinical decision making at the bedside.

HEALTH TECHNOLOGY ASSESSMENT AND CLINICAL DECISION MAKING:

1999 ◽  
Vol 15 (3) ◽  
pp. 585-592 ◽  
Author(s):  
Alicia Granados
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Technology Assessment ◽  
Case Studies ◽  
Technology Assessment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Health Technology ◽  
Evidence Based ◽  
Systematic Process

This paper examines the rationality of the concepts underlying evidence—based medicineand health technology assessment (HTA), which are part of a new current aimed at promoting the use of the results of scientific studies for decision making in health care. It describes the different approaches and purposes of this worldwide movement, in relation to clinical decision making, through a summarized set of specific HTA case studies from Catalonia, Spain. The examples illustrate how the systematic process of HTA can help in several types of uncertainties related to clinical decision making.

scholarly journals Physician-Specific Maximum Acceptable Risk in Personalized Medicine: Implications for Medical Decision Making

2018 ◽  
Vol 38 (5) ◽  
pp. 593-600
Author(s):  
Marco Boeri ◽  
Alan J. McMichael ◽  
Joseph P. M. Kane ◽  
Francis A. O’Neill ◽  
Frank Kee
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Discrete Choice Experiments ◽  
Medical Decision ◽  
Random Parameters ◽  
Acceptable Risk ◽  
Syndrome Scale ◽  
Unit Increase ◽  
Negative Syndrome

Background. In discrete-choice experiments (DCEs), respondents are presented with a series of scenarios and asked to select their preferred choice. In clinical decision making, DCEs allow one to calculate the maximum acceptable risk (MAR) that a respondent is willing to accept for a one-unit increase in treatment efficacy. Most published studies report the average MAR for the whole sample, without conveying any information about heterogeneity. For a sample of psychiatrists prescribing drugs for a series of hypothetical patients with schizophrenia, this article demonstrates how heterogeneity accounted for in the DCE modeling can be incorporated in the derivation of the MAR. Methods. Psychiatrists were given information about a group of patients’ responses to treatment on the Positive and Negative Syndrome Scale (PANSS) and the weight gain associated with the treatment observed in a series of 26 vignettes. We estimated a random parameters logit (RPL) model with treatment choice as the dependent variable. Results. Results from the RPL were used to compute the MAR for the overall sample. This was found to be equal to 4%, implying that, overall, psychiatrists were willing to accept a 4% increase in the risk of an adverse event to obtain a one-unit improvement of symptoms – measured on the PANSS. Heterogeneity was then incorporated in the MAR calculation, finding that MARs ranged between 0.5 and 9.5 across the sample of psychiatrists. Limitations. We provided psychiatrists with hypothetical scenarios, and their MAR may change when making decisions for actual patients. Conclusions. This analysis aimed to show how it is possible to calculate physician-specific MARs and to discuss how MAR heterogeneity could have implications for medical practice.

Clinical Decision Making by Emergency Room Physicians and Residents

2008 ◽  
pp. 217-223
Author(s):  
Ken J. Farion ◽  
Michael J. Hine ◽  
Wojtek Michalowski ◽  
Szymon Wilk
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Community ◽  
Medical Decision ◽  
Best Interest ◽  
Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

The impact of new technologies on clinical decision-making in health care

Science ◽  
2015 ◽  
Vol 350 (6266) ◽  
pp. 1397-1397
Author(s):  
R. Rosenquist Brandell ◽  
O. Kallioniemi ◽  
A. Wedell
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Decision ◽  
The Impact

scholarly journals Value-impregnated factual claims may undermine medical decision-making

2018 ◽  
Vol 13 (3) ◽  
pp. 151-158 ◽  
Author(s):  
Niels Lynøe ◽  
Gert Helgesson ◽  
Niklas Juth
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Clinical Decision Making ◽  
Negative Impact ◽  
Personal Values ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Medical Decision ◽  
Clinical Decisions ◽  
Soft Laws

Clinical decisions are expected to be based on factual evidence and official values derived from healthcare law and soft laws such as regulations and guidelines. But sometimes personal values instead influence clinical decisions. One way in which personal values may influence medical decision-making is by their affecting factual claims or assumptions made by healthcare providers. Such influence, which we call ‘value-impregnation,’ may be concealed to all concerned stakeholders. We suggest as a hypothesis that healthcare providers’ decision making is sometimes affected by value-impregnated factual claims or assumptions. If such claims influence e.g. doctor–patient encounters, this will likely have a negative impact on the provision of correct information to patients and on patients’ influence on decision making regarding their own care. In this paper, we explore the idea that value-impregnated factual claims influence healthcare decisions through a series of medical examples. We suggest that more research is needed to further examine whether healthcare staff’s personal values influence clinical decision-making.

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