A Cardiac Catheterization Laboratory Pulmonary Hypertension Protocol Significantly Improves the Quality of Clinical Data Available for Medical Decision-Making in Pulmonary Hypertension Patients.

2009 ◽  
Author(s):  
KM Nichols ◽  
T Manley ◽  
TJ Ryan ◽  
DC Burkey ◽  
JA Wirth
Keyword(s):  
Decision Making ◽  
Pulmonary Hypertension ◽  
Cardiac Catheterization ◽  
Clinical Data ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Catheterization Laboratory ◽  
Cardiac Catheterization Laboratory

Reducing overuse by recognising the unintended harms of good intentions

2019 ◽  
pp. bmjebm-2019-111247
Author(s):  
David Slawson ◽  
Allen F Shaughnessy
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Clinical Care ◽  
Critical Role ◽  
Medical Decision ◽  
Wide Acceptance ◽  
Hierarchy Of Evidence ◽  
Healthcare Interventions

Overdiagnosis and overtreatment—overuse—is gaining wide acceptance as a leading nosocomial intervention in medicine. Not only does overuse create anxiety and diminish patients’ quality of life, in some cases it causes harm to both patients and others not directly involved in clinical care. Reducing overuse begins with the recognition and acceptance of the potential for unintended harm of our best intentions. In this paper, we introduce five cases to illustrate where harm can occur as the result of well-intended healthcare interventions. With this insight, clinicians can learn to appreciate the critical role of probability-based, evidence-informed decision-making in medicine and the need to consider the outcomes for all who may be affected by their actions. Likewise, educators need to evolve medical education and medical decision-making so that it focuses on the hierarchy of evidence and that what ‘ought to work’, based on traditional pathophysiological, disease-focused reasoning, should be subordinate to what ‘does work’.

Incorporating patient preferences and priorities into end-of-life care.

2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 41-41
Author(s):  
Eric Rackow ◽  
Afua Ofori ◽  
Wendy Rodkey ◽  
Roy A. Beveridge
Keyword(s):  
Quality Of Life ◽  
Decision Making ◽  
End Of Life Care ◽  
Medical Decision Making ◽  
Case Managers ◽  
Readiness For Change ◽  
Medical Decision ◽  
Decision Making Process ◽  
Life Care

41 Background: Patients with advanced illness often face painful conversations and difficult decisions. A program was deployed to help patients identify, communicate, and incorporate their personal preferences and priorities into decisions about their care. The program was assessed by measuring movement along the readiness for change continuum. Methods: Patients residing in the home and participating in a chronic care program were referred by their case managers based on clinical conditions and whether the patient appeared to be in their last 12 months of life. Counseling sessions with patients or family caregiver/s were designed to move participants toward the following actions: be fully informed about their medical situation, describe their detailed quality of life priorities, articulate a self-defined medical decision making process, effectively communicate to their family and physicians, and implement and repeat the aforementioned steps. After 5 months (Sept-2014 to Feb-2015), movement along the readiness for change continuum (pre-contemplation, contemplation, preparation, action, maintenance, and advocacy) was reported. Results: Of the 427 patients referred, 33 could not be reached, 116 were ineligible, 50 declined or did not engage. Of the 228 participants, 191 (84%) moved at least one step in readiness for change continuum over the 5-month period. In Nov-2014, 13% of participants were in action, maintenance, or advocacy, which increased to 19% by Feb-2015. The largest observed movement to action, maintenance, or advocacy was in defining quality of life priorities: 2% Nov-2014 to 21% Feb-2015. The least movement to action, maintenance, or advocacy was observed in articulating a self-defined medical decision making process: 3% Nov-2014 to 16% Feb-2015. Case managers reported discomfort in referring members based on their assessment of length of life. Early surveys show high levels of satisfaction. Conclusions: A very high percentage of patients progressed in incorporating their preferences and priorities into end of life care as measured by the readiness to change continuum. This program is currently expanding and the referral process is changing from case manager to algorithm based identification referrals.

Modeling Deep Learning Neural Networks With Denotational Mathematics in UbiHealth Environment

2020 ◽  
Vol 12 (3) ◽  
pp. 14-27 ◽  
Author(s):  
John Sarivougioukas ◽  
Aristides Vagelatos
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Ubiquitous Computing ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Learning Networks ◽  
Healthcare Applications ◽  
Medical Diagnoses ◽  
The Given

Ubiquitous computing environments that are involved in healthcare applications are typically characterized by dynamically changing contexts. The contextual information must be efficiently processed in order to support medical decision making. The ubiquitous computing healthcare ecosystem must be capable of extracting medically valuable characteristics, making precise decisions, and taking medically appropriate actions. In this framework, deep learning networks can be used for data fusion of large and complex sets of information in order to make the appropriate medical diagnoses. The quality of decisions depends on the selection of appropriate network weights, which define a transformation of the given input into a diagnosis. Denotational mathematics provide a promising framework for modeling deep learning networks and adjusting their behavior by adapting their weights for the given input. Furthermore, the fidelity of the network's output can be controlled by applying a regulator to the weights values. The authors show that Denotational Mathematics can serve as a rigorous framework for modeling and controlling deep learning networks, thereby enhancing the quality of medical decision making.

Direct-Observation Cohort Study of Shared Decision Making in a Primary Care Clinic

2020 ◽  
Vol 40 (6) ◽  
pp. 756-765 ◽  
Author(s):  
Jeffrey L. Jackson ◽  
Derek Storch ◽  
Wilkins Jackson ◽  
Dorothy Becher ◽  
Patrick G. O’Malley
Keyword(s):  
Blood Pressure ◽  
Primary Care ◽  
Decision Making ◽  
Medical Decision Making ◽  
Primary Care Clinic ◽  
Small Sample ◽  
Medical Decision ◽  
Objective Measures ◽  
Care Clinic

Background. Observational studies suggest that shared medical decision making (SMDM) is suboptimal. Our objective was to assess patient preferences, ratings, and objective measurements of decision making and their impact on patient outcomes. Methods. Hypertensive adults presenting for routine care with their primary care physician completed previsit surveys assessing SMDM preferences. Postvisit surveys assessed the degree of SMDM during the encounter, patient satisfaction, and trust. Encounters were audiotaped and transcripts were coded for type of decisions made as well as SMDM quality using OPTION-5. Adherence and blood pressure were measured at baseline and at 4 weeks. Results. Among 105 encounters, there were 7.4 decisions per visit; most were basic, such as refills and routine testing. Objective measures of decision making indicated that the degree of SMDM was lower than reported by patients or physicians, although physician ratings were more accurate. Previsit, 54% of patients expressed a desire for equally shared medical decision making, 24% preferred physician dominated decision making, and 18% preferred that they make the decisions. Postvisit, patients reported experiencing SMDM in 57% of encounters, with high concordance between desired and perceived decision making. Discordance between the patient’s desired and experienced SMDM reduced trust and satisfaction. The quality of shared decisions had no impact on adherence or blood pressure at 4 weeks. Limitations. Single site, small sample. Conclusions. Decisions are common during internal medicine primary care visits, and most are basic. Most patients preferred SMDM, and their perceptions of the visit decision-making style were concordant with their preferences although higher than objective measures suggested. Physician ratings of the quality of SMDM were more accurate than patient ratings. Discordance between patients’ expected and experienced SMDM lowered satisfaction and trust.

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