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Potentially harmful medication dispenses after a fall or hip fracture: a mixed methods study of a commonly used quality measure.

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Fischer H, Hahn EE, Li BH, et al. Jt Comm J Qual Patient Saf. 2022;48:222-232.

While falls are common in older adults, there was a 31% increase in death due to falls in the U.S. from 2007-2016, partially associated with the increase in older adults in the population. This mixed methods study looked at the prevalence, risk factors, and contributors to potentially harmful medication dispensed after a fall/fracture of patients using the Potentially Harmful Drug-Disease Interactions in the Elderly (HEDIS DDE) codes. There were 113,809 patients with a first time fall; 35.4% had high-risk medications dispensed after their first fall. Interviews with 22 physicians identified patient reluctance to report falls and inconsistent assessment, and documentation of falls made it challenging to consider falls when prescribing medications.

Patient safety when receiving telephone advice in primary care - a Swedish qualitative interview study.

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Berntsson K, Eliasson M, Beckman L. BMC Nurs. 2022;21:24.

Safe and accurate telephone triage is of critical importance, particularly during the COVID-19 pandemic. This Swedish study evaluated district nurses’ experiences and perceptions of patient safety at a national nurse advice triage call center. Interviews with nurses resulted in an overall theme of “being able to make the right decision” based on the categories of “communication” and “assessment.”

Veterans Health Administration Stratification Tool for Opioid Risk Mitigation (STORM) Shows Promise for Targeting Prevention Interventions to Reduce Mortality in Patients Who Are Prescribed Opioids

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January 7, 2022

The Veterans Health Administration (VHA) Stratification Tool for Opioid Risk Mitigation (STORM) decision support system and targeted prevention program were designed to help mitigate risk factors for overdose and suicide among veterans who are prescribed opioids and/or with opioid use disorder (OUD) and are served by the VHA.¹ Veterans, particularly those prescribed opioids, experience overdose and suicide events at roughly twice the rate of the general population.^1,2

Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics.

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Orenstein EW, Kandaswamy S, Muthu N, et al. J Am Med Inform Assoc. 2021;28:2654-2660.

Alert fatigue is a known contributor to medical error. In this cross-sectional study, researchers found that custom alerts were responsible for the majority of alert burden at six pediatric health systems. This study also compared the use of different alert burden metrics to benchmark burden across and within institutions.

Medication Errors in Retail Pharmacies: Wrong Patient, Wrong Instructions.

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July 28, 2021

This commentary presents two cases highlighting common medication errors in retail pharmacy settings and discusses the importance of mandatory counseling for new medications, use of standardized error reporting processes, and the role of clinical decision support systems (CDSS) in medical decision-making and ensuring medication safety.

Can decision support combat incompleteness and bias in routine primary care data?

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Kostopoulou O, Tracey C, Delaney BC. J Am Med Inform Assoc. 2021;28:1461-1467.

In addition to being used for patient-specific clinical purposes, data within the electronic health record (EHR) may be used for other purposes including epidemiological research. Researchers in the UK developed and tested a clinical decision support system (CDSS) to evaluate changes in the types and number of observations that primary care physicians entered into the EHR during simulated patient encounters. Physicians documented more clinical observations using the CDSS compared to the standard electronic health record. The increase in documented clinical observations has the potential to improve validity of research developed from EHR data.

The development and piloting of the Ambulatory Electronic Health Record Evaluation Tool: lessons learned.

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Co Z, Holmgren AJ, Classen DC, et al. Appl Clin Inform. 2021;12:153-163.

Medication errors occur frequently in ambulatory care settings. This article describes the development and testing of an ambulatory medication safety evaluation tool, which is based on an inpatient version administered by The Leapfrog Group. Pilot testing at seven clinics around the US indicates that clinics struggled in areas of advanced decision support such as drug age and drug monitoring, and that most clinics lacked EHR-based medication reconciliation functions.

Reducing inappropriate outpatient medication prescribing in older adults across electronic health record systems.

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Friebe MP, LeGrand JR, Shepherd BE, et al. Appl Clin Inform. 2020;11:865-872.

The prescribing of potentially inappropriate medications, particularly among older adults, is an ongoing quality and safety concern. Among adults 65 years and older, this study found that clinical decision support integrated with a new electronic health record system significantly reduced potentially inappropriate medications.

Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial.

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Delvaux N, Piessens V, Burghgraeve TD, et al. Implement Sci. 2020;15:100.

Clinical decision support systems (CDSS) and computerized physician order entry (CPOE) have the potential to improve patient safety. This randomized trial evaluated the impact of integrating CDSS into CPOE among general practitioners in Belgium. The intervention improved appropriateness and decreased volume of laboratory test ordering and did not show any increases in diagnostic errors.

Use of an electronic decision support tool to reduce polypharmacy in elderly people with chronic diseases: cluster randomised controlled trial.

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Rieckert A, Reeves D, Altiner A, et al. BMJ. 2020;369:m1822.

This study evaluated the impact of an electronic decision support tool comprising a comprehensive drug review to support deprescribing and reduce polypharmacy in elderly adults. Results indicate that the tool did reduce the number of prescribed drugs but did not significantly reduce unplanned hospital admissions or death after 24 months.

Patient safety and quality improvement: ethical principles for a regulatory approach to bias in healthcare machine learning.

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McCradden MD, Joshi S, Anderson JA, et al. J Am Med Info Asso. 2020;27:2024-2027.

The authors discuss the challenges social inequalities pose to machine learning models, provide several recommendations for adopting ethical principles for the delivery of machine learning-assisted health care.

COVID-19: patient safety and quality improvement skills to deploy during the surge.

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Staines A, Amalberti R, Berwick DM, et al. Int J Qual Health Care. 2021;33:mzaa050.

The authors of this editorial propose a five-step strategy for patient safety and quality improvement staff to leverage their skills to support patients, staff, and organizations during the COVID-19 pandemic. It includes (1) strengthening the system and environment, (2) supporting patient, family and community engagement and empowerment, (3) improving clinical care through separation of workflows and development of clinical decision support, (4) reducing harm by proactively managing risk for patients with and without COVID-19, and (5) enhancing and expanding the learning system to develop resilience.

Use of an electronic clinical decision support system in primary care to assess inappropriate polypharmacy in young seniors with multimorbidity: observational, descriptive, cross-sectional study

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Rogero-Blanco E, Lopez-Rodriguez JA, Sanz-Cuesta T, et al. JMIR Med Inform. 2020;8.

Older patients are vulnerable to adverse drug events due to comorbidities and polypharmacy. This cross-sectional study from Spain reviewed prescriptions for 593 older adults aged 65-75 years with multiple comorbidities and documented polypharmacy to estimate the prevalence of potentially inappropriate prescribing using the STOPP and Beers Criteria. Potentially inappropriate prescribing was detected in over half of patients. The most frequently detected inappropriate prescriptions were for prolonged use of benzodiazepines (36% of patients) and prolonged use of proton pump inhibitors (45% of patients). Multiple risk factors associated with potentially inappropriate prescribing were identified, including polypharmacy and use of central nervous system drugs.

Making Healthcare Safer III.

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Holmes A, Long A, Wyant B, et al. Rockville, MD: Agency for Healthcare Research and Quality; March 2020. AHRQ Publication No. 20-0029-EF.

This newly issued follow up to the seminal AHRQ Making Health Care Safer report (first published in 2001 and updated in 2013 critically examines the evidence supporting 47 separate patient safety practices chosen for the high-impact harms they address. It includes diagnostic errors, failure to rescue, sepsis, infections due to multi-drug resistant organisms, adverse drug events and nursing-sensitive conditions. The report discusses the evidence on cross-cutting safety practices, including safety culture, teamwork and team training, clinical decision support, patient and family engagement, cultural competency, staff education and training, and monitoring, audit and feedback. The report provides recommendations for clinicians and decision-makers on effective patient safety practices.

How common mental shortcuts can cause major physician errors.

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Jena AB, Olenski AR. New York Times. February 20, 2020.

Unconscious biases affecting health care decisions elevate the potential for harm. This news story discusses how experience and implicit biases can impact physician decision-making. The use of decision support is one strategy highlighted to redirect heuristics and other cognitive biases to minimize their impact on treatment.

Using a machine learning system to identify and prevent medication prescribing errors: a clinical and cost analysis evaluation.

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Rozenblum R, Rodriguez-Monguio R, Volk LA, et al. Jt Comm J Qual Patient Saf. 2019;46:3-10.

Clinical decision support (CDS) tools help identify and reduce medication errors but are limited by the rules and types of errors programmed into their alerting logic and their high alerting rates and false positives, which can contribute to alert fatigue. This retrospective study evaluates the clinical validity and value of using a machine learning system (MedAware) for CDS as compared to an existing CDS system. Chart-reviewed MedAware alerts were accurate (92%) and clinically valid (79.7%). Overall, 68.2% of MedAware alerts would not have been generated by the CDS tool and estimated cost savings associated with the adverse events potentially prevented via MedAware alerts were substantial ($60/drug alert).

Investigation into Electronic Prescribing and Medicines Administration Systems and Safe Discharge.

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Farnborough, UK: Healthcare Safety Investigation Branch; 2019.

Design flaws and improper use of technologies that transfer medication and prescription information between provider environments is a known threat to patient safety. This report analyzes an anticoagulant overdose incident and found that information technology missteps contributed to the error.

The computer will see you now.

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Whitaker P. New Statesman. August 2, 2019;148:38-43.

Artificial intelligence (AI) and advanced computing technologies can enhance clinical decision-making. Exploring the strengths and weaknesses of artificial intelligence, this news article cautions against the wide deployment of AI until robust evaluation and implementation strategies are in place to enhance system reliability. A recent PSNet perspective discussed emerging safety issues in the use of artificial intelligence.

A systematic review of clinical decision support systems for clinical oncology practice.

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Pawloski PA, Brooks GA, Nielsen ME, et al. J Natl Compr Canc Netw. 2019;17:331-338.

In this systematic review, researchers sought to evaluate the impact of clinical decision support (CDS) on the quality and safety of care provided to cancer patients. Most of the studies included in the study demonstrated improved outcomes related to the use of CDS, but the authors conclude that more rigorous research regarding the impact of CDS on clinical outcomes for oncology patients is needed.

Testing and improving the acceptability of a web-based platform for collective intelligence to improve diagnostic accuracy in primary care clinics.

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Fontil V, Radcliffe K, Lyson HC, et al. JAMIA Open. 2019;2:40-48.

The use of collective intelligence platforms may have the potential to improve diagnostic accuracy in primary care, but little is known about the attitudes of primary care providers toward such platforms. This qualitative study found that primary care providers might be willing to use such platforms as long as they are easy to use, perceived as helpful and accurate, and that the collective opinions generated can be trusted.

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