Feasibility of incorporating genomic knowledge into electronic medical records for pharmacogenomic clinical decision support

AbstractThis editorial provides context for a series of published case reports in ACI Open by summarizing activities and outputs of joint electronic health record integration and pharmacogenomics workgroups in the NIH-funded electronic Medical Records and Genomics (eMERGE) Network. A case report is a useful tool to describe the range of capabilities that an IT infrastructure or a particular technology must support. The activities we describe have informed infrastructure requirements used during eMERGE phase III, provided a venue to share experiences and ask questions among other eMERGE sites, summarized potential hazards that might be encountered for specific clinical decision support (CDS) implementation scenarios, and provided a simple framework that captured progress toward implementing CDS at eMERGE sites in a consistent format.

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How do general practitioners access guidelines and utilise electronic medical records to make clinical decisions on antibiotic use? Results from an Australian qualitative study

BMJ Open ◽

10.1136/bmjopen-2018-028329 ◽

2019 ◽

Vol 9 (8) ◽

pp. e028329 ◽

Cited By ~ 5

Author(s):

Ruby Biezen ◽

Cassandra Roberts ◽

Kirsty Buising ◽

Karin Thursky ◽

Douglas Boyle ◽

...

Keyword(s):

Decision Support ◽

Qualitative Study ◽

Electronic Medical Records ◽

General Practitioners ◽

Clinical Decision Support ◽

Medical Records ◽

Clinical Decision ◽

Antibiotic Prescribing ◽

Evidence Based ◽

Practice Culture

ObjectiveThis study aimed to explore how general practitioners (GPs) access and use both guidelines and electronic medical records (EMRs) to assist in clinical decision-making when prescribing antibiotics in Australia.DesignThis is an exploratory qualitative study with thematic analysis interpreted using the Theory of Planned Behaviour (TPB) framework.SettingThis study was conducted in general practice in Victoria, Australia.ParticipantsTwenty-six GPs from five general practices were recruited to participate in five focus groups between February and April 2018.ResultsGPs expressed that current EMR systems do not provide clinical decision support to assist with antibiotic prescribing. Access and use of guidelines were variable. GPs who had more clinical experience were less likely to access guidelines than younger and less experienced GPs. Guideline use and guideline-concordant prescribing was facilitated if there was a practice culture encouraging evidence-based practice. However, a lack of access to guidelines and perceived patients’ expectation and demand for antibiotics were barriers to guideline-concordant prescribing. Furthermore, guidelines that were easy to access and navigate, free, embedded within EMRs and fit into the clinical workflow were seen as likely to enhance guideline use.ConclusionsCurrent barriers to the use of antibiotic guidelines include GPs’ experience, patient factors, practice culture, and ease of access and cost of guidelines. To reduce inappropriate antibiotic prescribing and to promote more rational use of antibiotic in the community, guidelines should be made available, accessible and easy to use, with minimal cost to practicing GPs. Integration of evidence-based antibiotic guidelines within the EMR in the form of a clinical decision support tool could optimise guideline use and increase guideline-concordant prescribing.

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Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa279 ◽

2020 ◽

Author(s):

Bethany A Van Dort ◽

Wu Yi Zheng ◽

Vivek Sundar ◽

Melissa T Baysari

Keyword(s):

Decision Support ◽

Electronic Medical Records ◽

Clinical Decision Support ◽

Medical Records ◽

Web Of Science ◽

Clinical Decision ◽

Internal Governance ◽

Effective Strategies ◽

Multiple Strategies ◽

Governance Processes

Abstract Objective To identify and summarize the current internal governance processes adopted by hospitals, as reported in the literature, for selecting, optimizing, and evaluating clinical decision support (CDS) alerts in order to identify effective approaches. Materials and methods Databases (Medline, Embase, CINAHL, Scopus, Web of Science, IEEE Xplore Digital Library, CADTH, and WorldCat) were searched to identify relevant papers published from January 2010 to April 2020. All paper types published in English that reported governance processes for selecting and/or optimizing CDS alerts in hospitals were included. Results Eight papers were included in the review. Seven papers focused specifically on medication-related CDS alerts. All papers described the use of a multidisciplinary committee to optimize alerts. Other strategies included the use of clinician feedback, alert data, literature and drug references, and a visual dashboard. Six of the 8 papers reported evaluations of their CDS alert modifications following the adoption of optimization strategies, and of these, 5 reported a reduction in alert rate. Conclusions A multidisciplinary committee, often in combination with other approaches, was the most frequent strategy reported by hospitals to optimize their CDS alerts. Due to the limited number of published processes, variation in system changes, and evaluation results, we were unable to compare the effectiveness of different strategies, although employing multiple strategies appears to be an effective approach for reducing CDS alert numbers. We recommend hospitals report on descriptions and evaluations of governance processes to enable identification of effective strategies for optimization of CDS alerts in hospitals.

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OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv091 ◽

2015 ◽

Vol 23 (2) ◽

pp. 339-348 ◽

Cited By ~ 21

Author(s):

Jonathan H Chen ◽

Tanya Podchiyska ◽

Russ B Altman

Keyword(s):

Decision Support ◽

Hospital Admission ◽

Electronic Medical Records ◽

Clinical Decision Support ◽

Medical Records ◽

Life Support ◽

Clinical Decision ◽

Grand Challenge ◽

Temporal Relationships ◽

Common Items

Abstract Objective: To answer a “grand challenge” in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com’s product recommender. Materials and Methods: EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender’s ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Results: Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% ( P < 10 −10 ) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% ( P < 10 −16 ). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Discussion: Recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from “correct” ones. Conclusions: Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. “interesting” suggestions).

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Utility of clinical technology-processes for developing countries

Clinical Governance An International Journal ◽

10.1108/cgij-04-2014-0019 ◽

2014 ◽

Vol 19 (3) ◽

pp. 253-268 ◽

Cited By ~ 1

Author(s):

Nikunj Agarwal ◽

M.P. Sebastian

Keyword(s):

Developing Countries ◽

Decision Support ◽

Clinical Decision Support ◽

Medical Records ◽

Positive Relationship ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Content Type ◽

The Usa

Purpose – The purpose of this paper is to evaluate the utility of clinical processes in healthcare institutions of different sizes. The implications of adoption rate of computerized physicians order entry (CPOE) and electronic medical/health records (EMRs/EHRs) in different sized healthcare institutions in the USA were studied in terms of understanding its impact on enhancement of quality of patient care. Design/methodology/approach – This study has used secondary data to obtain insights on the processes and technologies used in hospitals of different sizes in the USA and enlighten those in the developing countries to adopt a strategy that would be most appropriate for them. The Dorenfest Institute for H.I.T. Research and Education Analytics database (The Dorenfest Institute, 2011) provided the data for 5,038 US hospitals. Logistic regression was performed to study the impact of the different types of processes and technologies on institutions of different sizes, classified based on the number of beds, physicians, and nurses. Findings – The findings show that small sized hospitals had a positive relationship with drug dosing interactions process and nursing and clinician content process. On the contrary, medium sized hospitals had a negative relationship with the usage of CPOE for entering medical records, i.e. <25 percent (p<0.05). In order to be effective, these institutions should increase the usage of EMRs by more than 25 percent to get positive outcomes. Large hospitals showed a positive relationship with the usage of >75 percent of CPOE to enter medical records and usage of medical records >75 percent. Practical implications – The authors demonstrate the need for an evaluation of utility of acute care hospitals based on hospital size in terms of number of physicians, and nurses, which have not been dealt earlier by the past studies. Moreover, there is also a need for an evaluation of utility of acute care hospitals for implementation of CPOEs and EMRs that are integrated with clinical decision support systems. Originality/value – Although the data are US-centric, the insights provided by the results are very much relevant to the Indian scenario to support the improvement of the quality of care. The findings may help those implementing processes in healthcare institutions in India. No study has addressed the measurement of the positive and negative outcomes arising due to the implementation of different percentages of CPOEs and EMRs in different sized institutions. Further the number of physicians and nurses have not been considered earlier. Therefore, the authors have classified the hospitals based on physicians and nurses and studied their impact on the adoption of CPOEs, clinical decision support systems, and EMRs.

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A unique framework for the Persian clinical guidelines: addressing an evidence-based CDSS development need

BMJ evidence-based medicine ◽

10.1136/bmjebm-2019-111187 ◽

2019 ◽

Vol 25 (1) ◽

pp. 22-26 ◽

Cited By ~ 1

Author(s):

Hesam Karim ◽

Mohammad Hosseini Ravandi ◽

Zahra Zandesh ◽

Ahmad Naserpoor ◽

Mobin Yasini ◽

...

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Electronic Medical Records ◽

Medical Records ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Heterogeneous Structure ◽

Medical Decisions ◽

Unique Framework

Background and aimOne of the prerequisites to develop Computerised Decision Support Systems is Clinical Practice Guidelines (CPGs) which provide a systematic aid to make complex medical decisions. In order to provide an automated CPG, it is needed to have a unique structure for the CPGs. This study aims to propose a unique framework for the Persian guidelines.Materials and methods20 Persian CPGs were selected and divided into the creation and validation sets (n=10 for each). The first group was studied independently and their headings were listed; wherever possible, the headings were merged into a new heading that was applicable to all the guidelines. The developed framework was validated by the second group of the guidelines.ResultsStudied guidelines had a very heterogeneous structure. The number of original headings was 249; they were reduced to 14 main headings with 16 subheadings in a unique developed framework. The framework is able to represent and cover 100% of the guidelines.ConclusionThe heterogeneity of guidelines was high as they were not developed based on the unique framework. The proposed framework provides a layout for designing the CPGs with a homogeneous structure. Guideline developers can use this framework to develop structured CPGs. This will facilitate the integration of the guidelines into electronic medical records as well as clinical decision support systems.

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Clinical Decision Support for Diabetes Care in the Hospital: A Time for Change Toward Improvement of Management and Outcomes

Journal of Diabetes Science and Technology ◽

10.1177/1932296820982661 ◽

2021 ◽

pp. 193229682098266

Author(s):

Ariana R. Pichardo-Lowden

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Medical Records ◽

Diabetes Management ◽

Clinical Decision ◽

Daily Basis ◽

Healthcare Organizations ◽

Effective Care ◽

Individualize Care ◽

Glycemic Management

The increasing prevalence of diabetes permeates hospitals and dysglycemia is associated with poor clinical and economic outcomes. Despite endorsed guidelines, barriers to optimal management and gaps in care prevail. Providers’ limitations on knowledge, attitudes, and decision-making about hospital diabetes management are common. This adds to the complexity of dispersed glucose and insulin dosing data within medical records. This creates a dichotomy as safe and effective care are key objectives of healthcare organizations. This perspective highlights evidence of the benefits of clinical decision support (CDS) in hospital glycemic management. It elaborates on barriers CDS can help resolve, and factors driving its success. CDS represents a resource to individualize care and improve outcomes. It can help overcome a multifactorial problem impacting patients’ lives on a daily basis.

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