The Human Resource Costs of Implementing Autopend Clinical Decision Support to Improve Health Maintenance

2020 ◽  
Vol 26 (7) ◽  
pp. e232-e236
Keyword(s):  
Decision Support ◽  
Human Resource ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Maintenance ◽  
Resource Costs ◽  
Improve Health

scholarly journals Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

2016 ◽  
Vol 25 (S 01) ◽  
pp. S103-S116 ◽  
Author(s):  
D. F. Sittig ◽  
A. Wright ◽  
B. Middleton
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Technology Implementation ◽  
Information Technologies ◽  
Healthcare Delivery ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Clinical Workflow ◽  
Health Maintenance ◽  
Effective Use

Summary Objective: The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040. Method: Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.Result: In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery. Conclusion: CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

Using EHR-Based Clinical Decision Support to Provide Social Risk-Informed Care in Community Health Centers: Study Protocol and Design (Preprint)

2021 ◽  
Author(s):  
Rachel Gold ◽  
Christina Sheppler ◽  
Danielle Hessler ◽  
Arwen Bunce ◽  
Erika Cottrell ◽  
...  
Keyword(s):  
Decision Support ◽  
Health Outcomes ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Intervention Group ◽  
Clinical Decision ◽  
Health Centers ◽  
Social Risk ◽  
Social Risks ◽  
Improve Health

BACKGROUND Consistent and compelling evidence demonstrates that social and economic adversity impact health outcomes. In response, many healthcare professional organizations recommend screening patients for experiences of social and economic adversity or ‘social risks’—e.g., food, housing, and transportation insecurity—in the context of care. The guidance on how healthcare providers can act on documented social risk data to improve health outcomes is nascent. One strategy recommended by the National Academy of Medicine involves using social risk data to adapt care plans in ways that accommodate patients’ social risks. OBJECTIVE This study’s aims are to (1) develop electronic health record-based clinical decision support (CDS) tools that suggest social risk-informed care plan adaptations for patients with diabetes and/or hypertension; (2) assess tool adoption and its impact on selected Clinical Quality Measures in community health centers; and (3) examine perceptions of tool usability and impact on care quality. METHODS A systematic scoping review and several stakeholder activities will be conducted to inform development of the CDS tools. The tools will be pilot tested to obtain user input, and their content and form revised based on this input. A randomized quasi-experimental design will then be used to assess the revised tools’ impact. Eligible clinics will be randomized to a control group or potential intervention group; clinics will be recruited from the potential intervention group in a random order until six are enrolled in the study. Intervention clinics will have access to the CDS tools in their EHR, will receive minimal implementation support, and will be followed for 18 months to evaluate tool adoption and the impact of tool use on patient blood pressure and glucose control. RESULTS This study was funded in January 2020 by the National Institute on Minority Health and Health Disparities of the National Institutes of Health. Formative activities will take place from April 2020-July 2021; the CDS tools will be developed May 2021-November 2022; the pilot study will be conducted August 2021-July 2022; and the main trial will occur December 2022-May 2024. Study data will be analyzed, and results disseminated, in 2024. CONCLUSIONS Patients’ social risk information must be presented to care teams in a way that facilitates social risk-informed care. To our knowledge, this study is the first to develop and test EHR-embedded CDS tools designed to support the provision of social risk-informed care. Study results will add needed understanding of how to use social risk data to improve health outcomes and reduce disparities.

User and Task Analysis for Evaluation of Clinical Decision Support for Quality Improvement

2020 ◽  
Vol 64 (1) ◽  
pp. 750-754
Author(s):  
Swaminathan Kandaswamy ◽  
Dean Karavite ◽  
Naveen Muthu ◽  
Gerald Shaeffer ◽  
Robert Grundmeier ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Task Analysis ◽  
Healthcare Delivery ◽  
Clinical Decision ◽  
Clinical Knowledge ◽  
Quality Outcomes ◽  
Health Related ◽  
Improve Health ◽  
And Task

Clinical decision support (CDS) is a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information that can significantly improve health outcomes and healthcare delivery. However, their impact on clinical outcomes has been inconsistent. Rigorous and continuous evaluation of CDS is necessary for improving CDS. A User and Task analysis was conducted to understand the stakeholder roles, their goals and tasks involved in the evaluation of CDS. This study describes a framework for evaluating CDS effectiveness for improving quality outcomes based on the analysis.

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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