scholarly journals The Morningside Initiative: Collaborative Development of a Knowledge Repository to Accelerate Adoption of Clinical Decision Support

2010 ◽  
Vol 4 (1) ◽  
pp. 278-290 ◽  
Author(s):  
Robert Greenes ◽  
Meryl Bloomrosen ◽  
Nancy E. Brown-Connolly ◽  
Clayton Curtis ◽  
Don E Detmer ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Organizations ◽  
Clinical Decision ◽  
Advanced Technology ◽  
Veterans Health ◽  
Health Administration ◽  
American Medical Informatics Association ◽  
Public And Private Sector

The Morningside Initiative is a public-private activity that has evolved from an August, 2007, meeting at the Morningside Inn, in Frederick, MD, sponsored by the Telemedicine and Advanced Technology Research Center (TATRC) of the US Army Medical Research Materiel Command. Participants were subject matter experts in clinical decision support (CDS) and included representatives from the Department of Defense, Veterans Health Administration, Kaiser Permanente, Partners Healthcare System, Henry Ford Health System, Arizona State University, and the American Medical Informatics Association (AMIA). The Morningside Initiative was convened in response to the AMIA Roadmap for National Action on Clinical Decision Support and on the basis of other considerations and experiences of the participants. Its formation was the unanimous recommendation of participants at the 2007 meeting which called for creating a shared repository of executable knowledge for diverse health care organizations and practices, as well as health care system vendors. The rationale is based on the recognition that sharing of clinical knowledge needed for CDS across organizations is currently virtually non-existent, and that, given the considerable investment needed for creating, maintaining and updating authoritative knowledge, which only larger organizations have been able to undertake, this is an impediment to widespread adoption and use of CDS. The Morningside Initiative intends to develop and refine (1) an organizational framework, (2) a technical approach, and (3) CDS content acquisition and management processes for sharing CDS knowledge content, tools, and experience that will scale with growing numbers of participants and can be expanded in scope of content and capabilities. Intermountain Healthcare joined the initial set of participants shortly after its formation. The efforts of the Morningside Initiative are intended to serve as the basis for a series of next steps in a national agenda for CDS. It is based on the belief that sharing of knowledge can be highly effective as is the case in other competitive domains such as genomics. Participants in the Morningside Initiative believe that a coordinated effort between the private and public sectors is needed to accomplish this goal and that a small number of highly visible and respected health care organizations in the public and private sector can lead by example. Ultimately, a future collaborative knowledge sharing organization must have a sustainable long-term business model for financial support.

scholarly journals Towards a Maturity Model for Clinical Decision Support Operations

2019 ◽  
Vol 10 (05) ◽  
pp. 810-819
Author(s):  
Evan W. Orenstein ◽  
Naveen Muthu ◽  
Asli O. Weitkamp ◽  
Daria F. Ferro ◽  
Mike D. Zeidlhack ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Organizations ◽  
Health Care Systems ◽  
Model Development ◽  
Clinical Decision ◽  
Maturity Model ◽  
Iterative Model ◽  
Care Systems

AbstractClinical decision support (CDS) systems delivered through the electronic health record are an important element of quality and safety initiatives within a health care system. However, managing a large CDS knowledge base can be an overwhelming task for informatics teams. Additionally, it can be difficult for these informatics teams to communicate their goals with external operational stakeholders and define concrete steps for improvement. We aimed to develop a maturity model that describes a roadmap toward organizational functions and processes that help health care systems use CDS more effectively to drive better outcomes. We developed a maturity model for CDS operations through discussions with health care leaders at 80 organizations, iterative model development by four clinical informaticists, and subsequent review with 19 health care organizations. We ceased iterations when feedback from three organizations did not result in any changes to the model. The proposed CDS maturity model includes three main “pillars”: “Content Creation,” “Analytics and Reporting,” and “Governance and Management.” Each pillar contains five levels—advancing along each pillar provides CDS teams a deeper understanding of the processes CDS systems are intended to improve. A “roof” represents the CDS functions that become attainable after advancing along each of the pillars. Organizations are not required to advance in order and can develop in one pillar separately from another. However, we hypothesize that optimal deployment of preceding levels and advancing in tandem along the pillars increase the value of organizational investment in higher levels of CDS maturity. In addition to describing the maturity model and its development, we also provide three case studies of health care organizations using the model for self-assessment and determine next steps in CDS development.

Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements

2021 ◽  
Vol 12 (01) ◽  
pp. 182-189
Author(s):  
Adam Wright ◽  
Skye Aaron ◽  
Allison B. McCoy ◽  
Robert El-Kareh ◽  
Daniel Fort ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Organizations ◽  
Software Tool ◽  
Clinical Decision ◽  
Unintended Consequences ◽  
Boolean Logic ◽  
Safety Issues ◽  
Automated Algorithm ◽  
Logic Minimization

Abstract Objective Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements. Methods Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors. Results Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback. Discussion An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations. Conclusion Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.

Organizational Factors Influencing the Use of Clinical Decision Support for Improving Cancer Screening Within Community Health Centers

2016 ◽  
pp. 118-148 ◽  
Author(s):  
Timothy Jay Carney ◽  
Michael Weaver ◽  
Anna M. McDaniel ◽  
Josette Jones ◽  
David A. Haggstrom
Keyword(s):  
Colorectal Cancer ◽  
Health Care ◽  
Decision Support ◽  
Cancer Screening ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Clinical Decision ◽  
Health Centers ◽  
Practice Setting

Adoption of clinical decision support (CDS) systems leads to improved clinical performance through improved clinician decision making, adherence to evidence-based guidelines, medical error reduction, and more efficient information transfer and to reduction in health care disparities in under-resourced settings. However, little information on CDS use in the community health care (CHC) setting exists. This study examines if organizational, provider, or patient level factors can successfully predict the level of CDS use in the CHC setting with regard to breast, cervical, and colorectal cancer screening. This study relied upon 37 summary measures obtained from the 2005 Cancer Health Disparities Collaborative (HDCC) national survey of 44 randomly selected community health centers. A multi-level framework was designed that employed an all-subsets linear regression to discover relationships between organizational/practice setting, provider, and patient characteristics and the outcome variable, a composite measure of community health center CDS intensity-of-use. Several organizational and provider level factors from our conceptual model were identified to be positively associated with CDS level of use in community health centers. The level of CDS use (e.g., computerized reminders, provider prompts at point-of-care) in support of breast, cervical, and colorectal cancer screening rate improvement in vulnerable populations is determined by both organizational/practice setting and provider factors. Such insights can better facilitate the increased uptake of CDS in CHCs that allows for improved patient tracking, disease management, and early detection in cancer prevention and control within vulnerable populations.

Advanced Issues of Health Informatics and Clinical Decision Support System in Global Health Care

2018 ◽  
pp. 153-170 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Global Health ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Health Informatics ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Global Health Care

This chapter indicates the advanced issues of health informatics; the advanced issues of Clinical Decision Support System (CDSS); CDSS and Computerized Physician Order Entry (CPOE); the false positive alerts in CDSS; and CDSS and biomedical engineering. Health informatics and CDSS are the advanced health care technologies with the support of many technological fields. Health informatics and CDSS apply various computerized devices to provide enhanced health-related outcomes in terms of problem solving, analytical thinking, and decision making. Health informatics and CDSS help clinicians and health care providers to make complex information useful in supporting clinical decisions, thus delivering the best standard of care for each patient. The chapter argues that utilizing health informatics and CDSS has the potential to increase health outcomes and reach strategic goals in global health care.

scholarly journals P036: A clinical decision support intervention to increase usage of probenecid in the ED

CJEM ◽  
2016 ◽  
Vol 18 (S1) ◽  
pp. S90-S90
Author(s):  
S. Dowling ◽  
E. Lang ◽  
D. Wang ◽  
T. Rich
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Soft Tissue ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Soft Tissue Infections ◽  
Post Intervention ◽  
Support Intervention ◽  
Intervention Phase ◽  
Decision Support Intervention

Introduction: In certain circumstances, skin and soft tissue infections are managed with intravenous (IV) antibiotics. In our center, patients initiated on outpatient IV antibiotics are followed up by a home parental therapy program the following day. A significant number of these patients require a repeat visit to the ED because of clinic hours. Probenecid is a drug that can prolong the half-life of certain antibiotics (such as cefazolin) and can therefore avoid a repeat ED visit, reducing health care costs and improve ED capacity. Our goal was to increase probenecid usage in the ED in order to optimize management of skin and soft tissue infections (SSTI) in the ED. The primary outcome was to compare the usage of probenecid in the pre and post-intervention phase. Secondary outcomes were to compare revisit rates between patients receiving cefazolin alone vs cefazolin + probenecid. Methods: Using administrative data merged with Computerized Physician Order Entry (CPOE), we extracted data 90 days pre- and 90 post-intervention (February 11, 2015 to August 11, 2015). The setting for the study is an urban center (4 adult ED’s with an annual census of over 320,000 visits per year). Our CPOE system is fully integrated into the ED patient care. The multi-faceted intervention involved modifying all relevant SSTI order sets in the CPOE system to link any cefazolin order with an order for probenecid. Physicians and nurses were provided with a 1 page summary of probenecid (indications, contra-indications, pharmacology), as well as decision support with the CPOE. Any patients who were receiving outpatient cefazolin therapy were included in the study. Results: Our analysis included 2512 patients (1148 and 1364 patients in the pre/post phases) who received cefazolin in the ED and were discharged during the 180 day period. Baseline variables (gender, age, % admitted) and ED visits were similar in both phases. In the pre-intervention phase 30.2% of patients received probenecid and in the post-intervention phase 43.0%, for a net increase of 12.8% (p=<0.0001). Patients who received probenecid had a 2.2% (11.4% vs 13.6%, p=0.014) lower re-visit rate in the following 72H. Conclusion: We have implemented a CPOE based clinical decision support intervention that demonstrated significant increase in probenecid usage by emergency physician and resulted in a decrease in ED revisits. This intervention would result in health care cost-savings.

scholarly journals Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

2011 ◽  
Vol 02 (03) ◽  
pp. 284-303 ◽  
Author(s):  
A. Wright ◽  
M. Burton ◽  
G. Fraser ◽  
M. Krall ◽  
S. Maviglia ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Health Care ◽  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Diabetes Care ◽  
Clinical Decision ◽  
The United States ◽  
Computer Based

SummaryBackground: Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known.Objective: We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care.Methods: We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites.Results: The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines.Conclusion: Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

scholarly journals Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis

2018 ◽  
Vol 38 (4) ◽  
pp. 46-54 ◽  
Author(s):  
Devida Long ◽  
Muge Capan ◽  
Susan Mascioli ◽  
Danielle Weldon ◽  
Ryan Arnold ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
User Interface ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Care Providers

BACKGROUND Hospitals are increasingly turning to clinical decision support systems for sepsis, a life-threatening illness, to provide patient-specific assessments and recommendations to aid in evidence-based clinical decision-making. Lack of guidelines on how to present alerts has impeded optimization of alerts, specifically, effective ways to differentiate alerts while highlighting important pieces of information to create a universal standard for health care providers. OBJECTIVE To gain insight into clinical decision support systems–based alerts, specifically targeting nursing interventions for sepsis, with a focus on behaviors associated with and perceptions of alerts, as well as visual preferences. METHODS An interactive survey to display a novel user interface for clinical decision support systems for sepsis was developed and then administered to members of the nursing staff. RESULTS A total of 43 nurses participated in 2 interactive survey sessions. Participants preferred alerts that were based on an established treatment protocol, were presented in a pop-up format, and addressed the patient’s clinical condition rather than regulatory guidelines. CONCLUSIONS The results can be used in future research to optimize electronic medical record alerting and clinical practice workflow to support the efficient, effective, and timely delivery of high-quality care to patients with sepsis. The research also may advance the knowledge base of what information health care providers want and need to improve the health and safety of their patients.

scholarly journals CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support

2019 ◽  
Vol 10 (01) ◽  
pp. 001-009 ◽  
Author(s):  
Barbara Jones ◽  
Dave Collingridge ◽  
Caroline Vines ◽  
Herman Post ◽  
John Holmen ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Health Care System ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision ◽  
Additional Patient ◽  
Free Text ◽  
Learning Health Care System ◽  
Care System

Background Local implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process. Objectives This article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations. Methods We implemented a multicenter bedside CDS for the emergency department management of pneumonia that integrated patient data with guideline-based recommendations. We examined the frequency of adoption versus rejection of recommendations for site-of-care and antibiotic selection. We analyzed free-text responses provided by physicians explaining their clinical reasoning for rejection, using concept mapping and thematic analysis. Results Among 1,722 patient episodes, physicians rejected recommendations to send a patient home in 24%, leaving text in 53%; reasons for rejection of the recommendations included additional or alternative diagnoses beyond pneumonia, and comorbidities or signs of physiologic derangement contributing to risk of outpatient failure that were not processed by the CDS. Physicians rejected broad-spectrum antibiotic recommendations in 10%, leaving text in 76%; differences in pathogen risk assessment, additional patient information, concern about antibiotic properties, and admitting physician preferences were given as reasons for rejection. Conclusion While adoption of CDS recommendations for pneumonia was high, physicians rejecting recommendations frequently provided feedback, reporting alternative diagnoses, additional individual patient characteristics, and provider preferences as major reasons for rejection. CDS that collects user feedback is feasible and can contribute to a learning health system.

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