On Data Quality and Risk in Guideline Based Clinical Decision Support

Abstract Introduction Physicians spend more than half of their workday interacting with health information systems to care for their patients. Effective data management that provides physicians with comprehensive patient information from various information systems is required to ensure high quality clinical decision making.Objectives We evaluated the impact of a novel, CE-certified clinical decision support tool on physician’s effectiveness and satisfaction in the clinical decision-making process.Methods Using pre-therapeutic prostate cancer management cases, we compared physician’s expenditure of time, data quality, and user satisfaction in the decision-making process comparing the current standard with the software. Ten urologists from our department conducted the diagnostic work-up to the treatment decision for a total of 10 patients using both approaches.Results A significant reduction in the physician’s expenditure of time for the decision-making process by -59.9 % (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated data quality parameters completeness (Cohen's d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94).Conclusion The software demonstrated that effective data management can improve physician’s effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.

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A thematic analysis to examine the feasibility of EHR-based clinical decision support for implementing Choosing Wisely® guidelines

JAMIA Open ◽

10.1093/jamiaopen/ooab031 ◽

2021 ◽

Vol 4 (2) ◽

Author(s):

Brian J Douthit ◽

Catherine J Staes ◽

Guilherme Del Fiol ◽

Rachel L Richesson

Keyword(s):

Decision Support ◽

Data Quality ◽

Clinical Decision Support ◽

Thematic Analysis ◽

Clinical Decision ◽

Choosing Wisely ◽

Complex Nature ◽

Individual Level ◽

Qualitative Thematic Analysis ◽

Implementation Planning

Abstract Objective To identify important barriers and facilitators relating to the feasibility of implementing clinical practice guidelines (CPGs) as clinical decision support (CDS). Materials and Methods We conducted a qualitative, thematic analysis of interviews from seven interviews with dyads (one clinical expert and one systems analyst) who discussed the feasibility of implementing 10 Choosing Wisely® guidelines at their institutions. We conducted a content analysis to extract salient themes describing facilitators, challenges, and other feasibility considerations regarding implementing CPGs as CDS. Results We identified five themes: concern about data quality impacts implementation planning; the availability of data in a computable format is a primary factor for implementation feasibility; customized strategies are needed to mitigate uncertainty and ambiguity when translating CPGs to an electronic health record-based tool; misalignment of expected CDS with pre-existing clinical workflows impact implementation; and individual level factors of end-users must be considered when selecting and implementing CDS tools. Discussion The themes reveal several considerations for CPG as CDS implementations regarding data quality, knowledge representation, and sociotechnical issues. Guideline authors should be aware that using CDS to implement CPGs is becoming increasingly popular and should consider providing clear guidelines to aid implementation. The complex nature of CPG as CDS implementation necessitates a unified effort to overcome these challenges. Conclusion Our analysis highlights the importance of cooperation and co-development of standards, strategies, and infrastructure to address the difficulties of implementing CPGs as CDS. The complex interactions between the concepts revealed in the interviews necessitates the need that such work should not be conducted in silos. We also implore that implementers disseminate their experiences.

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Interventions to improve the use of EMRs in primary health care: a systematic review and meta-analysis

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2019-000023 ◽

2019 ◽

Vol 26 (1) ◽

pp. e000023

Author(s):

Noura Hamade ◽

Amanda Terry ◽

Monali Malvankar-Mehta

Keyword(s):

Systematic Review ◽

Decision Support ◽

Data Quality ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Primary Healthcare ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Healthcare Settings

BackgroundElectronic medical record (EMR) adoption in primary care has grown exponentially since their introduction in the 1970s. However, without their proper use benefits cannot be achieved. This includes: 1) the complete and safe documentation of patient information; 2) improved coordination of care; 3) reduced errors and 4) more involved patients. The use of EMRs is defined by practitioners using EMRs and their features to perform daily practice functions.ObjectiveThe purpose of this systematic review was to identify interventions aimed at improving EMR use in primary healthcare settings.MethodsTen online databases were searched to identify studies conducted in primary healthcare settings aimed at implementing interventions to observe the use of EMRs and directly measure the use of EMR functions or outcomes effected by the use of EMR functions.ResultsOf 2098 identified studies, 12 were included in the review. Results showed that interventions focused on the use of EMR functions, including referrals, electronic communication, reminders, use of clinical decision support systems and workflow management support functions, were five times more likely to show improvements in EMR use compared with controls. Interventions focused on data quality were five and a half times more likely to show improvements in EMR use compared with controls.ConclusionsIndividuals in primary healthcare settings aiming to improve EMR use would benefit from implementing interventions focused on EMR feature add-ons such as clinical decision support systems and customised referral templates, and provisions of educational materials, or financial incentives targeted at improving the use of EMR functions and data quality.

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From Bedside to Bytes and Back: Data Quality and Standardization for Research, Quality Improvement, and Clinical Decision Support in the Era of Electronic Health Records*

Pediatric Critical Care Medicine ◽

10.1097/pcc.0000000000002366 ◽

2020 ◽

Vol 21 (8) ◽

pp. 780-781

Author(s):

L. Nelson Sanchez-Pinto ◽

Adam C. Dziorny

Keyword(s):

Quality Improvement ◽

Decision Support ◽

Electronic Health Records ◽

Data Quality ◽

Clinical Decision Support ◽

Clinical Decision ◽

Research Quality ◽

Health Records ◽

Electronic Health

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Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative Analysis

Applied Clinical Informatics ◽

10.1055/s-0041-1732401 ◽

2021 ◽

Vol 12 (04) ◽

pp. 710-720

Author(s):

David A. Dorr ◽

Christopher D'Autremont ◽

Christie Pizzimenti ◽

Nicole Weiskopf ◽

Robert Rope ◽

...

Keyword(s):

Blood Pressure ◽

Decision Support ◽

Data Quality ◽

High Blood Pressure ◽

Clinical Decision Support ◽

Clinical Decision ◽

Unintended Consequences ◽

Use Cases ◽

Patient Specific ◽

Imperfect Data

Abstract Objective This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools. Methods We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS. Results We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9–11.2%), low exclusion (0–0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting. Discussion Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases. Conclusion Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences.

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