Electronic Decision Support for Deprescribing in Patients on Hemodialysis: Clinical Research Protocol for a Prospective, Controlled, Quality Improvement Study.
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SUBMITTER: Bortolussi-Courval E
PROVIDER: S-EPMC10331104 | biostudies-literature | 2023
REPOSITORIES: biostudies-literature
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Electronic Decision Support for Deprescribing in Patients on Hemodialysis: Clinical Research Protocol for a Prospective, Controlled, Quality Improvement Study.
Bortolussi-Courval Émilie É Podymow Tiina T Trinh Emilie E Moryousef Joseph J Hanula R R Huon Jean-François JF Mavrakanas Thomas T Suri Rita R Lee Todd C TC McDonald Emily Gibson EG
Canadian journal of kidney health and disease 20230626
<h4>Background</h4>Patients on dialysis are commonly prescribed multiple medications (polypharmacy), many of which are potentially inappropriate medications (PIMs). Potentially inappropriate medications are associated with an increased risk of falls, fractures, and hospitalization. MedSafer is an electronic tool that generates individualized, prioritized reports with deprescribing opportunities by cross-referencing patient health data and medications with guidelines for deprescribing.<h4>Objecti ...[more]
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Project description:Rationale & objectivePatients treated with dialysis are commonly prescribed multiple medications (polypharmacy), including some potentially inappropriate medications (PIMs). PIMs are associated with an increased risk of medication harm (eg, falls, fractures, hospitalization). Deprescribing is a solution that proposes to stop, reduce, or switch medications to a safer alternative. Although deprescribing pairs well with routine medication reviews, it can be complex and time-consuming. Whether clinical decision support improves the process and increases deprescribing for patients treated with dialysis is unknown. This study aimed to test the efficacy of the clinical decision support software MedSafer at increasing deprescribing for patients treated with dialysis.Study designProspective controlled quality improvement study with a contemporaneous control.Setting & participantsPatients prescribed ≥5 medications in 2 outpatient dialysis units in Montréal, Canada.ExposuresPatient health data from the electronic medical record were input into the MedSafer web-based portal to generate reports listing candidate PIMs for deprescribing. At the time of a planned biannual medication review (usual care), treating nephrologists in the intervention unit additionally received deprescribing reports, and patients received EMPOWER brochures containing safety information on PIMs they were prescribed. In the control unit, patients received usual care alone.Analytical approachThe proportion of patients with ≥1 PIMs deprescribed was compared between the intervention and control units following a planned medication review to determine the effect of using MedSafer. The absolute risk difference with 95% CI and number needed to treat were calculated.OutcomesThe primary outcome was the proportion of patients with one or more PIMs deprescribed. Secondary outcomes include the reduction in the mean number of prescribed drugs and PIMs from baseline.ResultsIn total, 195 patients were included (127, control unit; 68, intervention unit); the mean age was 64.8 ± 15.9 (SD), and 36.9% were women. The proportion of patients with ≥1 PIMs deprescribed in the control unit was 3.1% (4/127) vs 39.7% (27/68) in the intervention unit (absolute risk difference, 36.6%; 95% CI, 24.5%-48.6%; P < 0.0001; number needed to treat = 3).LimitationsThis was a single-center nonrandomized study with a type 1 error risk. Deprescribing durability was not assessed, and the study was not powered to reduce adverse drug events.ConclusionsDeprescribing clinical decision support and patient EMPOWER brochures provided during medication reviews could be an effective and scalable intervention to address PIMs in the dialysis population. A confirmatory randomized controlled trial is needed.
| S-EPMC11019279 | biostudies-literature
Project description:ImportanceScalable deprescribing interventions may reduce polypharmacy and the use of potentially inappropriate medications (PIMs); however, few studies have been large enough to evaluate the impact that deprescribing may have on adverse drug events (ADEs).ObjectiveTo evaluate the effect of an electronic deprescribing decision support tool on ADEs after hospital discharge among older adults with polypharmacy.Design, setting, and participantsThis was a cluster randomized clinical trial of older (≥65 years) hospitalized patients with an expected survival of more than 3 months who were admitted to 1 of 11 acute care hospitals in Canada from August 22, 2017, to January 13, 2020. At admission, participants were taking 5 or more medications per day. Data analyses were performed from January 3, 2021, to September 23, 2021.InterventionsPersonalized reports of deprescribing opportunities generated by MedSafer software to address usual home medications and measures of prognosis and frailty. Deprescribing reports provided to the treating team were compared with usual care (medication reconciliation).Main outcomes and measuresThe primary outcome was a reduction of ADEs within the first 30 days postdischarge (including adverse drug withdrawal events) captured through structured telephone surveys and adjudicated blinded to intervention status. Secondary outcomes were the proportion of patients with 1 or more PIMs deprescribed at discharge and the proportion of patients with an adverse drug withdrawal event (ADWE).ResultsA total of 5698 participants (median [range] age, 78 [72-85] years; 2858 [50.2%] women; race and ethnicity data were not collected) were enrolled in 3 clusters and were adjudicated for the primary outcome (control, 3204; intervention, 2494). Despite cluster randomization, there were group imbalances, eg, the participants in the intervention arm were older and had more PIMS prescribed at baseline. After hospital discharge, 4989 (87.6%) participants completed an ADE interview. There was no significant difference in ADEs within 30 days of discharge (138 [5.0%] of 2742 control vs 111 [4.9%] of 2247 intervention participants; adjusted risk difference [aRD] -0.8%; 95% CI, -2.9% to 1.3%). Deprescribing increased from 795 (29.8%) of 2667 control to 1249 (55.4%) of 2256 intervention participants [aRD, 22.2%; 95% CI, 16.9% to 27.4%]. There was no difference in ADWEs between groups. Several post hoc sensitivity analyses, including the use of a nonparametric test to address the low cluster number, group imbalances, and potential biases, did not alter study conclusions.Conclusions and relevanceThis cluster randomized clinical trial showed that providing deprescribing clinical decision support during acute hospitalization had no demonstrable impact on ADEs, although the intervention was safe and led to improvements in deprescribing.Trial registrationClinicalTrials.gov Identifier: NCT03272607.
| S-EPMC8767487 | biostudies-literature
Project description:Large gaps exist in the implementation of guideline recommendations for cardiovascular disease (CVD) risk management. Electronic decision support (EDS) systems are promising interventions to close these gaps but few have undergone clinical trial evaluation in Australia. We have developed HealthTracker, a multifaceted EDS and quality improvement intervention to improve the management of CVD risk. It is hypothesised that the use of HealthTracker over a 12-month period will result in: (1) an increased proportion of patients receiving guideline-indicated measurements of CVD risk factors and (2) an increased proportion of patients at high risk will receive guideline-indicated prescriptions for lowering their CVD risk. Sixty health services (40 general practices and 20 Aboriginal Community Controlled Health Services (ACCHSs) will be randomised in a 1:1 allocation to receive either the intervention package or continue with usual care, stratified by service type, size and participation in existing quality improvement initiatives. The intervention consists of point-of-care decision support; a risk communication interface; a clinical audit tool to assess performance on CVD-related indicators; a quality improvement component comprising peer-ranked data feedback and support to develop strategies to improve performance. The control arm will continue with usual care without access to these intervention components. Quantitative data will be derived from cross-sectional samples at baseline and end of study via automated data extraction. Detailed process and economic evaluations will also be conducted. The general practice component of the study is approved by the University of Sydney Human Research Ethics Committee (HREC) and the ACCHS component is approved by the Aboriginal Health and Medical Research Council HREC. Formal agreements with each of the participating sites have been signed. In addition to the usual scientific forums, results will be disseminated via newsletters, study websites, face-to-face feedback forums and workshops. The trial is registered with the Australian Clinical Trials Registry ACTRN 12611000478910.
| S-EPMC3533097 | biostudies-literature
Project description:We describe quality improvement and practice-based research using the electronic medical record (EMR) in a community health system-based department of neurology. Our care transformation initiative targets 10 neurologic disorders (brain tumors, epilepsy, migraine, memory disorders, mild traumatic brain injury, multiple sclerosis, neuropathy, Parkinson disease, restless legs syndrome, and stroke) and brain health (risk assessments and interventions to prevent Alzheimer disease and related disorders in targeted populations). Our informatics methods include building and implementing structured clinical documentation support tools in the EMR; electronic data capture; enrollment, data quality, and descriptive reports; quality improvement projects; clinical decision support tools; subgroup-based adaptive assignments and pragmatic trials; and DNA biobanking. We are sharing EMR tools and deidentified data with other departments toward the creation of a Neurology Practice-Based Research Network. We discuss practical points to assist other clinical practices to make quality improvements and practice-based research in neurology using the EMR a reality.
| S-EPMC4634157 | biostudies-literature
Project description:The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.
| S-EPMC4416392 | biostudies-literature
Project description:BackgroundPrimary immunodeficiencies (PIs) are a set of heterogeneous chronic disorders characterized by immune dysfunction. They are diagnostically challenging because of their clinical heterogeneity, knowledge gaps among primary care physicians, and continuing shortages of clinically trained immunologists. As a result, patients with undiagnosed PIs are at increased risk for recurrent infections, cancers, and autoimmune diseases.ObjectiveThe aim of this research is to develop and implement a clinical decision support (CDS) tool for the identification of underlying PIs.MethodsWe will develop and implement a CDS tool for the identification of underlying PIs among patients who receive primary care through a health care provider at the University of Iowa Hospitals and Clinics. The CDS tool will function through an algorithm that is based on the Immune Deficiency Foundation's 10 Warning Signs for Primary Immunodeficiency. Over the course of a year, we will use Lean Six Sigma principles and the Define, Measure, Analyze, Improve, and Control (DMAIC) framework to guide the project. The primary measure is the number of newly diagnosed PI patients per month. Secondary measures include the following: (1) the number of new patients identified by the CDS as being at high risk for PI, (2) the number of new PI cases in which immunoglobulin replacement or rotating antibiotics are started, (3) the cost of evaluation of each patient identified by the CDS tool as being at high risk for PIs, (4) the number of new consults not diagnosed with a PI, and (5) patient satisfaction with the process of referral to the Immunology Clinic.ResultsThis study was determined to not be Human Subjects Research by the Institutional Review Board at the University of Iowa. Data collection will begin in August 2021.ConclusionsThe development and implementation of a CDS tool is a promising approach to identifying patients with underlying PI. This protocol assesses whether such an approach will be able to achieve its objective of reducing diagnostic delays. The disciplined approach, using Lean Six Sigma and the DMAIC framework, will guide implementation to maximize opportunities for a successful intervention that meets the study's goals and objectives as well as to allow for replication and adaptation of these methods at other sites.International registered report identifier (irrid)PRR1-10.2196/32635.
| S-EPMC8767470 | biostudies-literature
Project description:BackgroundThere is growing international emphasis on deprescribing, involving the monitored reduction or stopping of medications that are no longer needed or that cause more harm than benefits, especially for the elderly. Community pharmacists are well positioned to partner with patients and their other health care providers in facilitating deprescribing activities.ObjectiveTo build community pharmacists' capacity to integrate deprescribing into their daily practices through training and workflow strategies.MethodsThis study used an exploratory mixed-methods (primarily qualitative) design. Staff at 4 Ontario pharmacies were trained to use deprescribing guidelines. Qualitative data were collected through field observations, notes from advisory group meetings and documented Plan-Do-Study-Act (PDSA) plans. Quantitative data were derived from process and output measures reported by the pharmacies. Iterative PDSA cycles allowed the project team to appraise and accelerate process improvements over time and to summarize findings on facilitators, barriers and the adaptation of processes.ResultsAll 4 pharmacies identified individual and common goals related to deprescribing; however, drugs targeted and use of professional services to identify and address deprescribing opportunities varied. Each demonstrated that deprescribing activities could be integrated into daily practice and workflow. Common themes characterized approaches taken by each pharmacy: (1) processes used for capacity building among staff to identify patients for possible deprescribing, (2) approaches for preliminary interactions with patients, (3) in-depth medication reviews and (4) follow-up and monitoring. Approaches changed over time.ConclusionDeprescribing appears to be feasible in community pharmacies. Data derived to populate a business model canvas informs the development of an in-depth business model for deprescribing. Can Pharm J (Ott) 2019;152:xx-xx.
| S-EPMC6966265 | biostudies-literature
Project description:BackgroundDespite growing evidence that deprescribing can improve clinical outcomes, quality of life and reduce the likelihood of adverse drug events, the practice is not widespread, particularly in hospital settings. Clinical risk assessment tools, like the Drug Burden Index (DBI), can help prioritise patients for medication review and prioritise medications to deprescribe, but are not integrated within routine care. The aim of this study was to conduct formative usability testing of a computerised decision support (CDS) tool, based on DBI, to identify modifications required to the tool prior to trialling in practice.MethodsOur CDS tool comprised a DBI MPage in the electronic medical record (clinical workspace) that facilitated review of a patient's DBI and medication list, access to deprescribing resources, and the ability to deprescribe. Two rounds of scenario-based formative usability testing with think-aloud protocol were used. Seventeen end-users participated in the testing, including junior and senior doctors, and pharmacists.ResultsParticipants expressed positive views about the DBI CDS tool but testing revealed a number of clear areas for improvement. These primarily related to terminology used (i.e. what is a DBI and how is it calculated?), and consistency of functionality and display. A key finding was that users wanted the CDS tool to look and function in a similar way to other decision support tools in the electronic medical record. Modifications were made to the CDS tool in response to user feedback.ConclusionUsability testing proved extremely useful for identifying components of our CDS tool that were confusing, difficult to locate or to understand. We recommend usability testing be adopted prior to implementation of any digital health intervention. We hope our revised CDS tool equips clinicians with the knowledge and confidence to consider discontinuation of inappropriate medications in routine care of hospitalised patients. In the next phase of our project, we plan to pilot test the tool in practice to evaluate its uptake and effectiveness in supporting deprescribing in routine hospital care.
| S-EPMC8022373 | biostudies-literature
Project description:BackgroundStroke is a major cause of death and disability worldwide. Reducing the incidence of stroke has the potential to not only improve health outcomes, but also lead to significant cost savings for health services. Transient ischaemic attacks (TIA) can herald an imminent stroke and following a TIA early initiation of best medical therapy significantly reduces the risk of subsequent stroke. To achieve time targets rapid access stroke specialist services have been promoted; however, a number of resource related barriers can impede specialist access and cause unnecessary time delays. Cross sector collaboration led to the development of a primary care based TIA/Stroke electronic decision support (EDS) tool. This study aimed to assess the impact of this tool on improving access and reducing management delays.MethodsThis is a prospective before (2009) versus after (2011) study of the effect on process of care following the implementation of EDS assisted TIA management in primary care. All patients presenting with TIA to secondary services were included. Outcomes assessed were TIA Guideline adherence and patient safety.ResultsOver the study period 266 patients presented for TIA assessment (130 in 2009 and 136 in 2011). Following EDS implementation the median delay to specialist assessment fell from 10 days in 2009 to three days in 2011 (HR 1.45; 95% CI 1.13-1.86; p=0.001), the number of patients achieving optimal medical therapy within 24 hours rose from 43% to 57% (RR 1.33; 95% CI 1.02-1.71; p=0.04), carotid and CT imaging were achieved significantly faster (HR 1.52 (1.02-2.26) p=0.003 and HR 1.34 (1.16-1.78 p=0.002) respectively), and there were no adverse events associated with EDS use.ConclusionThe availability of TIA/Stroke electronic decision support in the primary care setting was associated with reductions in management delays without compromising patient safety.
| S-EPMC4070650 | biostudies-literature
Project description:BackgroundHealth worker compliance with established best-practice clinical and public health guidelines may be enhanced by customized checklists of care and clinical decision support driven by point-of-care data entry into an electronic health registry. The public health system of Palestine is currently implementing a national electronic registry (eRegistry) for maternal and child health. This trial is embedded in the national implementation and aims to assess the effectiveness of the eRegistry's interactive checklists and clinical decision support, compared with the existing paper based records, on improving the quality of care for pregnant women.MethodsThis two-arm cluster randomized controlled trial is conducted in the West Bank, Palestine, and includes 120 clusters (primary healthcare clinics) with an average annual enrollment of 60 pregnancies. The intervention tool is the eRegistry's interactive checklists and clinical decision support implemented within the District Health Information System 2 (DHIS2) Tracker software, developed and customized for the Palestinian context. The primary outcomes reflect the processes of essential interventions, namely timely and appropriate screening and management of: 1) anemia in pregnancy; 2) hypertension in pregnancy; 3) abnormal fetal growth; 4) and diabetes mellitus in pregnancy. The composite primary health outcome encompasses five conditions representing risk for the mother or baby that could have been detected or prevented by high-quality antenatal care: moderate or severe anemia at admission for labor; severe hypertension at admission for labor; malpresentation at delivery undetected during pregnancy; small for gestational age baby at delivery undetected during pregnancy; and large for gestational age baby at delivery. Primary analysis at the individual level taking the design effect of the clustering into account will be performed as intention-to-treat.DiscussionThis trial, embedded in the national implementation of the eRegistry in Palestine, allows the assessment of process and health outcomes in a large-scale pragmatic setting. Findings will inform the use of interactive checklists and clinical decision support driven by point-of-care data entry into an eRegistry as a health systems-strengthening approach.Trial registrationISRCTN trial registration number, ISRCTN18008445 . Registered on 6 April 2017.
| S-EPMC5778657 | biostudies-literature