Clinical decision models in hospital and outpatient care

PrimaryCare ◽  
2015 ◽  
Vol 15 (23) ◽  
pp. 402-402
Keyword(s):  
Outpatient Care ◽  
Clinical Decision ◽  
Decision Models

Treatment of Uncertainty in an Oncology Protocol by Probabilistic and Artificial Intelligence Approaches

1988 ◽  
Vol 27 (01) ◽  
pp. 23-33 ◽  
Author(s):  
Fiorella de Rosis ◽  
G. Steve ◽  
C. Biagini ◽  
R. Maurizi-Enrici
Keyword(s):  
Artificial Intelligence ◽  
Sensitivity Analysis ◽  
Expert System ◽  
Probabilistic Model ◽  
Decision Process ◽  
Clinical Decision ◽  
Final Diagnosis ◽  
Decision Models ◽  
Expert System Shell ◽  
Subjective Estimation

SummaryThe decision process for diagnosis and treatment of Hodgkin’s disease at the Institute of Radiology of Rome has been modelled integrating the guidelines of a protocol with uncertainty aspects. Two models have been built, using a PROSPECTOR-like Expert System shell for microcomputers: the first of them treats the uncertainty by the inferential engine of the shell, the second is a probabilistic model. The decisions suggested in a group of simulated and real cases by a section of the two models have been compared with an “objective” final diagnosis; this analysis showed that, in some cases, the two models give different suggestions and that “approximations” of the shell’s inferential engine may induce wrong conclusions. A sensitivity analysis of the probabilistic model showed that the outputs are greatly influenced by variations of parameters, whose subjective estimation appears to be especially difficult. This experience gives the opportunity to consider the risks of building clinical decision models based on Expert System shells, if the assumptions and approximations hidden in the shell have not been previously analyzed in a careful and critical way.

Artificial Intelligence in Outpatient Care in Times of Covid-19: A Multidisciplinary Comment on the Impact of a Notional Artificial Intelligence Based Clinical Decision Support System on Outpatient Care for Kidney Transplant Recipients (Preprint)

2020 ◽  
Author(s):  
Bilgin Osmanodja ◽  
Matthias Braun ◽  
Aljoscha Burchardt ◽  
Wiebke Duettmann ◽  
Michelle Fiekens ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Kidney Transplant ◽  
Clinical Decision Support ◽  
Support System ◽  
Outpatient Care ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Transplant Recipients ◽  
Kidney Transplant Recipients

UNSTRUCTURED The Covid-19 pandemic has put new demands on the medical systems worldwide. The pressure of taking far-reaching decisions within multiply limited resources under the constraint that personal contact must be minimized has evoked the question if technical support in the form of Artificial Intelligence (AI) could help leverage these challenges. At the same time, AI comes with its own issues such as limited transparency that cannot be neglected especially in a medical context. We will deliberate this in the domain of specialized outpatient care of kidney transplant recipients. In order to improve long-term care for these patients, we implemented a telemedicine functionality monitoring vital signs, medication adherence and symptoms at Charité – Universitätsmedizin Berlin. This paper seeks to combine this established telemonitoring approach with methods from Artificial Intelligence proposing an AI-based clinical decision support system (AI-CDSS) that aims to detect Covid-19 and other severe diseases in this high-risk population. After analyzing medical needs and difficulties and suggesting possible technical solutions, we argue that AI-supported telemonitoring in outpatient care can play a valuable role in managing resources and risks in kidney transplant patients in times of Covid-19 and beyond. Additionally, regarding the multitude of ethical and legal questions arising when integrating AI into workflows, we exemplarily discuss the concept of meaningful human control and whether it is achievable with the proposed AI-CDSS.

Evidence-Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions

2020 ◽  
pp. 019459982094882
Author(s):  
Lisa Caulley ◽  
Myriam G. Hunink ◽  
Gregory W. Randolph ◽  
Jennifer J. Shin
Keyword(s):  
Decision Trees ◽  
Evidence Based Medicine ◽  
Markov Models ◽  
Clinical Care ◽  
Clinical Decision ◽  
Decision Models ◽  
Data Driven ◽  
Evidence Based ◽  
Modeling And Analysis ◽  
Based Medicine

Objective To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. Data Sources Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. Review Methods Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. Conclusions Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. Implications for Practice Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.

scholarly journals Decision Models and Technology Can Help Psychiatry Develop Biomarkers

2021 ◽  
Vol 12 ◽  
Author(s):  
Daniel S. Barron ◽  
Justin T. Baker ◽  
Kristin S. Budde ◽  
Danilo Bzdok ◽  
Simon B. Eickhoff ◽  
...  
Keyword(s):  
Clinical Data ◽  
Decision Model ◽  
Empirical Evaluation ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Decision Models ◽  
Phenotypic Data ◽  
Medical Specialties ◽  
Fundamental Reason ◽  
Improve Patient

Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care.

Preliminary Findings on the Use of Session Questionnaires to Improve Clinician-Client Alliance With Special Consideration to Clinical Education

2015 ◽  
Vol 25 (1) ◽  
pp. 50-60
Author(s):  
Anu Subramanian
Keyword(s):  
Graduate Students ◽  
Clinical Education ◽  
Clinical Decision Making ◽  
Rating Scale ◽  
Treatment Effectiveness ◽  
Clinical Decision ◽  
Added Value ◽  
Therapeutic Outcomes ◽  
Specific Feedback ◽  
Stakeholder Perspective

ASHA's focus on evidence-based practice (EBP) includes the family/stakeholder perspective as an important tenet in clinical decision making. The common factors model for treatment effectiveness postulates that clinician-client alliance positively impacts therapeutic outcomes and may be the most important factor for success. One strategy to improve alliance between a client and clinician is the use of outcome questionnaires. In the current study, eight parents of toddlers who attended therapy sessions at a university clinic responded to a session outcome questionnaire that included both rating scale and descriptive questions. Six graduate students completed a survey that included a question about the utility of the questionnaire. Results indicated that the descriptive questions added value and information compared to using only the rating scale. The students were varied in their responses regarding the effectiveness of the questionnaire to increase their comfort with parents. Information gathered from the questionnaire allowed for specific feedback to graduate students to change behaviors and created opportunities for general discussions regarding effective therapy techniques. In addition, the responses generated conversations between the client and clinician focused on clients' concerns. Involving the stakeholder in identifying both effective and ineffective aspects of therapy has advantages for clinical practice and education.

Clinical Decision-Making for Stroke and Aphasia in the Older Adult

2009 ◽  
Vol 14 (1) ◽  
pp. 4-11 ◽  
Author(s):  
Jacqueline Hinckley
Keyword(s):  
Executive Function ◽  
Cerebrovascular Disease ◽  
Cognitive Abilities ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cognitive Profile ◽  
Focal Lesion ◽  
Cognitive Profiles ◽  
Supportive Communication ◽  
Spaced Retrieval

Abstract A patient with aphasia that is uncomplicated by other cognitive abilities will usually show a primary impairment of language. The frequency of additional cognitive impairments associated with cerebrovascular disease, multiple (silent or diagnosed) infarcts, or dementia increases with age and can complicate a single focal lesion that produces aphasia. The typical cognitive profiles of vascular dementia or dementia due to cerebrovascular disease may differ from the cognitive profile of patients with Alzheimer's dementia. In order to complete effective treatment selection, clinicians must know the cognitive profile of the patient and choose treatments accordingly. When attention, memory, and executive function are relatively preserved, strategy-based and conversation-based interventions provide the best choices to target personally relevant communication abilities. Examples of treatments in this category include PACE and Response Elaboration Training. When patients with aphasia have co-occurring episodic memory or executive function impairments, treatments that rely less on these abilities should be selected. Examples of treatments that fit these selection criteria include spaced retrieval and errorless learning. Finally, training caregivers in the use of supportive communication strategies is helpful to patients with aphasia, with or without additional cognitive complications.

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