scholarly journals Effects of patients’ hospital discharge preferences on uptake of clinical decision support

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247270
Author(s):  
James C. Cox ◽  
Ira L. Leeds ◽  
Vjollca Sadiraj ◽  
Kurt E. Schnier ◽  
John F. Sweeney
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Hospital Discharge ◽  
Clinical Decision Support ◽  
Average Length ◽  
Hospital Readmissions ◽  
Standardized Patients ◽  
Clinical Decision ◽  
Decision Makers ◽  
Control Group

The Centers for Medicare and Medicaid Services identified unplanned hospital readmissions as a critical healthcare quality and cost problem. Improvements in hospital discharge decision-making and post-discharge care are needed to address the problem. Utilization of clinical decision support (CDS) can improve discharge decision-making but little is known about the empirical significance of two opposing problems that can occur: (1) negligible uptake of CDS by providers or (2) over-reliance on CDS and underuse of other information. This paper reports an experiment where, in addition to electronic medical records (EMR), clinical decision-makers are provided subjective reports by standardized patients, or CDS information, or both. Subjective information, reports of being eager or reluctant for discharge, was obtained during examinations of standardized patients, who are regularly employed in medical education, and in our experiment had been given scripts for the experimental treatments. The CDS tool presents discharge recommendations obtained from econometric analysis of data from de-identified EMR of hospital patients. 38 clinical decision-makers in the experiment, who were third and fourth year medical students, discharged eight simulated patient encounters with an average length of stay 8.1 in the CDS supported group and 8.8 days in the control group. When the recommendation was “Discharge,” CDS uptake of “Discharge” recommendation was 20% higher for eager than reluctant patients. Compared to discharge decisions in the absence of patient reports: (i) odds of discharging reluctant standardized patients were 67% lower in the CDS-assisted group and 40% lower in the control (no-CDS) group; whereas (ii) odds of discharging eager standardized patients were 75% higher in the control group and similar in CDS-assisted group. These findings indicate that participants were neither ignoring nor over-relying on CDS.

Comparison of Nursing Diagnostic Accuracy of a Knowledge-Based Clinical Decision Support System with Clinical Diagnostic Validity and Bayesian Decision Models for Psychiatric Care Plan Formulation among Nursing Students (Preprint)

2021 ◽  
Author(s):  
Kuei-Fang Ho ◽  
Po-Hsiang Chou ◽  
Min-Huey Chung
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Nursing Students ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Care Plan ◽  
Control Group ◽  
Bayesian Decision ◽  
Care Plans ◽  
Knowledge Based

BACKGROUND Information technology can improve patient care plans to achieve high-quality care. However, few information systems can strengthen decision-making competency in health care environments. The most suitable and reliable inference engines for clinical decision support systems in nursing clinical practice have rarely been explored. OBJECTIVE This study examined the nursing diagnostic accuracy of a knowledge-based clinical decision support system (KBCDSS) based on clinical diagnostic validity (CDV) and the Bayesian decision models for nursing students to use during a psychiatric–mental health nursing practicum. METHODS A single-blinded, nonequivalent control group pretest–posttest design was adopted. Participants were 607 nursing students. In the quasi-experimental design, 2 intervention groups using a KBCDSS with the CDV (n=203) or the Bayesian decision (n=198) inference engine and a control group (n=206) used the psychiatric care plan system without guidance indicators to support decision-making. RESULTS Positive predictive value (PPV) and sensitivity indicated that decision-making competency was the highest for those in the CDV group followed by the Bayesian decision (BADE) and control groups. Relative to the control group, both the CDV and BADE groups exhibited significantly higher 3Q model questionnaire and modified TAM3 scores. In terms of perceived usefulness and behavioral intention, the CDV group had significantly higher 3Q model and modified TAM3 scores than did the BADE group, which had significantly higher scores than the control group did. CONCLUSIONS KBCDSSs can be adopted to provide empirical patient-oriented information and help nursing students rapidly manage patient information and formulate patient-centered care plans.

scholarly journals Application of Clinical Decision Support System to Assist Breast Cancer Patients with Lifestyle Modifications during the COVID-19 Pandemic: A Randomised Controlled Trial

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 2115
Author(s):  
Panos Papandreou ◽  
Aristea Gioxari ◽  
Frantzeska Nimee ◽  
Maria Skouroliakou
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Control Group ◽  
Healthy Weight ◽  
Lifestyle Modifications ◽  
Randomised Controlled

Clinical decision support systems (CDSS) are data aggregation tools based on computer technology that assist clinicians to promote healthy weight management and prevention of cardiovascular diseases. We carried out a randomised controlled 3-month trial to implement lifestyle modifications in breast cancer (BC) patients by means of CDSS during the COVID-19 pandemic. In total, 55 BC women at stages I-IIIA were enrolled. They were randomly assigned either to Control group, receiving general lifestyle advice (n = 28) or the CDSS group (n = 27), to whom the CDSS provided personalised dietary plans based on the Mediterranean diet (MD) together with physical activity guidelines. Food data, anthropometry, blood markers and quality of life were evaluated. At 3 months, higher adherence to MD was recorded in the CDSS group, accompanied by lower body weight (kg) and body fat mass percentage compared to control (p < 0.001). In the CDSS arm, global health/quality of life was significantly improved at the trial endpoint (p < 0.05). Fasting blood glucose and lipid levels (i.e., cholesterol, LDL, triacylglycerols) of the CDSS arm remained unchanged (p > 0.05) but were elevated in the control arm at 3 months (p < 0.05). In conclusion, CDSS could be a promising tool to assist BC patients with lifestyle modifications during the COVID-19 pandemic.

“Send & hold” clinical decision support rules improvement to reduce unnecessary testing of vitamins A, E, K, B1, B2, B3, B6 and C

2018 ◽  
Vol 56 (7) ◽  
pp. 1063-1070 ◽  
Author(s):  
Enrique Rodriguez-Borja ◽  
Africa Corchon-Peyrallo ◽  
Esther Barba-Serrano ◽  
Celia Villalba Martínez ◽  
Arturo Carratala Calvo
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Primary Care Physicians ◽  
Vitamin B1 ◽  
Clinical Decision ◽  
Health Department ◽  
Leadership Role ◽  
Control Group ◽  
Vitamin B3 ◽  
The Impact

Abstract Background: We assessed the impact of several “send & hold” clinical decision support rules (CDSRs) within the electronical request system for vitamins A, E, K, B1, B2, B3, B6 and C for all outpatients at a large health department. Methods: When ordered through electronical request, providers (except for all our primary care physicians who worked as a non-intervention control group) were always asked to answer several compulsory questions regarding main indication, symptomatology, suspected diagnosis, vitamin active treatments, etc., for each vitamin test using a drop-down list format. After samples arrival, tests were later put on hold internally by our laboratory information system (LIS) until review for their appropriateness was made by two staff pathologists according to the provided answers and LIS records (i.e. “send & hold”). The number of tests for each analyte was compared between the 10-month period before and after CDSRs implementation in both groups. Results: After implementation, vitamins test volumes decreased by 40% for vitamin A, 29% for vitamin E, 42% for vitamin K, 37% for vitamin B1, 85% for vitamin B2, 68% for vitamin B3, 65% for vitamin B6 and 59% for vitamin C (all p values 0.03 or lower except for vitamin B3), whereas in control group, the majority increased or remained stable. In patients with rejected vitamins, no new requests and/or adverse clinical outcome comments due to this fact were identified. Conclusions: “Send & hold” CDSRs are a promising informatics tool that can support in utilization management and enhance the pathologist’s leadership role as tests specialist.

PP145 VALIDATE Methodology For A Medication-Related Clinical Decision Support System: Innovating Or Going Back To Basics?

2021 ◽  
Vol 37 (S1) ◽  
pp. 21-22
Author(s):  
Carla Fernandez-Barceló ◽  
Elena Calvo-Cidoncha ◽  
Laura Sampietro-Colom
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Health Technology ◽  
Problem Definition ◽  
Cochrane Library

IntroductionIn the past decade, health technology assessment (HTA) has narrowed its scope to the analysis of mainly clinical and economic benefits. However, twenty-first century technology challenges require the need for more holistic assessments to obtain accurate recommendations for decision-making, as it was in HTA's foundations. VALues In Doing Assessments of health TEchnologies (VALIDATE) methodology approaches complex technologies holistically to provide a deeper understanding of the problem through analysis of the heterogeneity of stakeholders’ views, allowing for more comprehensive HTAs. This study aimed to assess a pharmaceutical clinical decision support system (CDSS) using VALIDATE.MethodsA systematic review of the empirical evidence on CDSS was conducted according to PRISMA guidelines. PubMed, the Cochrane Library, and Web of Science databases were searched for literature published between 2000 and 2020. Additionally, a review of grey literature and semi-structured interviews with different hospital stakeholders (pharmacists, physicians, computer engineers, etc.) were conducted. Content analysis was used for data integration.ResultsPreliminary literature results indicated consensus regarding the effectiveness of CDSS. Nevertheless, when including multistakeholder views, CDSS appeared to not be fully accepted in clinical practice. The main reasons for this appeared to be alert fatigue and disruption of workflow. Preliminary results based on information from the literature were contrasted with stakeholder interview responses.ConclusionsIncorporation of facts and stakeholder values into the problem definition and scoping for a health technology is essential to properly conduct HTAs. The lack of an inclusive multistakeholder scoping can lead to inaccurate information, and in this particular case to suboptimal CDSS implementation concerning decision-making for the technology being evaluated.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

An Assessment of Federated Machine Learning for Translational Research

2020 ◽  
pp. 123-142
Author(s):  
Manoj A. Thomas ◽  
Diya Suzanne Abraham ◽  
Dapeng Liu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Translational Research ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
High Quality ◽  
Intermediate Data ◽  
Translational Informatics ◽  
Data Analytic

Translational research (TR) is the harnessing of knowledge from basic science and clinical research to advance healthcare. As a sister discipline, translational informatics (TI) concerns the application of informatics theories, methods, and frameworks to TR. This chapter builds upon TR concepts and aims to advance the use of machine learning (ML) and data analytics for improving clinical decision support. A federated machine learning (FML) architecture is described to aggregate multiple ML endpoints, and intermediate data analytic processes and products to output high quality knowledge discovery and decision making. The proposed architecture is evaluated for its operational performance based on three propositions, and a case for clinical decision support in the prediction of adult Sepsis is presented. The chapter illustrates contributions to the advancement of FML and TI.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

scholarly journals Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

2020 ◽  
Vol 11 (04) ◽  
pp. 570-577
Author(s):  
Santiago Romero-Brufau ◽  
Kirk D. Wyatt ◽  
Patricia Boyum ◽  
Mindy Mickelson ◽  
Matthew Moore ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
High Risk ◽  
Clinical Decision Support ◽  
Readmission Rate ◽  
Hospital Readmissions ◽  
Clinical Decision ◽  
Regional Hospital ◽  
Patient Centered ◽  
Readmission Rates

Abstract Background Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. Objective The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. Methods A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. Results Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. Conclusion We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.

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