Economic impact of clinical decision support interventions based on electronic health records

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.

Financial impact of medication reviews by clinical pharmacists to reduce in-hospital adverse drug events: a return-on-investment analysis

BACKGROUND

Adverse drug events contribute to rising health care costs. Clinical pharmacists can reduce their risks by identifying and solving drug-related problems (DRPs) through medication review.

AIM

To develop an economic model to determine whether medication reviews performed by clinical pharmacists could lead to a reduction in health care costs associated with the prevention of potential adverse drug events.

METHOD

Two pharmacists performed medication reviews during ward rounds in an internal medicine setting over one year. Avoided costs were estimated by monetizing five categories of DRPs (improper drug selection, drug interactions, untreated indications, inadequate dosages, and drug use without an indication). An expert panel assessed potential adverse drug events and their probabilities of occurrence for 20 randomly selected DRPs in each category. The costs of adverse drug events were extracted from internal hospital financial data. A partial economic study from a hospital perspective then estimated the annual costs avoided by resolving DRPs identified by 3 part-time clinical pharmacists (0.9 full-time equivalent) from 2019 to 2020. The return on investment (ROI) of medication review was calculated.

RESULTS

The estimated annual avoided costs associated with the potential adverse drug events induced by 676 DRPs detected was € 304,170. The cost of a 0.9 full-time equivalent clinical pharmacist was € 112,408. Extrapolated to 1 full-time equivalent, the annual net savings was € 213,069 or an ROI of 1-1.71. Sensitivity analyses showed that the economic model was robust.

CONCLUSION

This economic model revealed the positive financial impact and favorable return on investment of a medication review intervention performed by clinical pharmacists. These findings should encourage the future deployment of a pharmacist-led adverse drug events prevention program.

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Using Clinical Decision Support Systems to Decrease Intravenous Acetaminophen Use: Implementation and Lessons Learned

BACKGROUND

Clinical decision support systems (CDSS) can enhance medical decision-making by providing targeted information to providers. While they have the potential to improve quality of care and reduce costs, they are not universally effective and can lead to unintended harm.

OBJECTIVES

To describe the implementation of an unsuccessful interruptive CDSS that aimed to promote appropriate use of intravenous (IV) acetaminophen at an academic pediatric hospital, with an emphasis on lessons learned.

METHODS

Quality improvement methodology was used to study the effect of an interruptive CDSS, which set a mandatory expiry time of 24 hours for all IV acetaminophen orders. This CDSS was implemented on April 5, 2021. The primary outcome measure was number of IV acetaminophen administrations per 1,000 patient days, measured pre- and postimplementation. Process measures were the number of IV acetaminophen orders placed per 1,000 patient days. Balancing measures were collected via survey data and included provider and nursing acceptability and unintended consequences of the CDSS.

RESULTS

There was no special cause variation in hospital-wide IV acetaminophen administrations and orders after CDSS implementation, nor when the CDSS was removed. A total of 88 participants completed the survey. Nearly half (40/88) of respondents reported negative issues with the CDSS, with the majority stating that this affected patient care (39/40). Respondents cited delays in patient care and reduced efficiency as the most common negative effects.

CONCLUSION

This study underscores the significance of monitoring CDSS implementations and including end user acceptability as an outcome measure. Teams should be prepared to modify or remove CDSS that do not achieve their intended goal or are associated with low end user acceptability. CDSS holds promise for improving clinical practice, but careful implementation and ongoing evaluation are crucial for maximizing their benefits and minimizing potential harm.

Development and Pilot Implementation of Neotree, a Digital Quality Improvement Tool Designed to Improve Newborn Care and Survival in 3 Hospitals in Malawi and Zimbabwe: Cost Analysis Study

Background

Two-thirds of the 2.4 million newborn deaths that occurred in 2020 within the first 28 days of life might have been avoided by implementing existing low-cost evidence-based interventions for all sick and small newborns. An open-source digital quality improvement tool (Neotree) combining data capture with education and clinical decision support is a promising solution for this implementation gap.

Objective

We present results from a cost analysis of a pilot implementation of Neotree in 3 hospitals in Malawi and Zimbabwe.

Methods

We combined activity-based costing and expenditure approaches to estimate the development and implementation cost of a Neotree pilot in 1 hospital in Malawi, Kamuzu Central Hospital (KCH), and 2 hospitals in Zimbabwe, Sally Mugabe Central Hospital (SMCH) and Chinhoyi Provincial Hospital (CPH). We estimated the costs from a provider perspective over 12 months. Data were collected through expenditure reports, monthly staff time-use surveys, and project staff interviews. Sensitivity and scenario analyses were conducted to assess the impact of uncertainties on the results or estimate potential costs at scale. A pilot time-motion survey was conducted at KCH and a comparable hospital where Neotree was not implemented.

Results

Total cost of pilot implementation of Neotree at KCH, SMCH, and CPH was US $37,748, US $52,331, and US $41,764, respectively. Average monthly cost per admitted child was US $15, US $15, and US $58, respectively. Staff costs were the main cost component (average 73% of total costs, ranging from 63% to 79%). The results from the sensitivity analysis showed that uncertainty around the number of admissions had a significant impact on the costs in all hospitals. In Malawi, replacing monthly web hosting with a server also had a significant impact on the costs. Under routine (nonresearch) conditions and at scale, total costs are estimated to fall substantially, up to 76%, reducing cost per admitted child to as low as US $5 in KCH, US $4 in SMCH, and US $14 in CPH. Median time to admit a baby was 27 (IQR 20-40) minutes using Neotree (n=250) compared to 26 (IQR 21-30) minutes using paper-based systems (n=34), and the median time to discharge a baby was 9 (IQR 7-13) minutes for Neotree (n=246) compared to 3 (IQR 2-4) minutes for paper-based systems (n=50).

Conclusions

Neotree is a time- and cost-efficient tool, comparable with the results from limited similar mHealth decision-support tools in low- and middle-income countries. Implementation costs of Neotree varied substantially between the hospitals, mainly due to hospital size. The implementation costs could be substantially reduced at scale due to economies of scale because of integration to the health systems and reductions in cost items such as staff and overhead. More studies assessing the impact and cost-effectiveness of large-scale mHealth decision-support tools are needed.

Managing Resource Utilization Cost of Laboratory Tests for Patients on Chemotherapy in Johns Hopkins Aramco Healthcare

Introduction

Laboratory testing is a fundamental diagnostic and prognostic tool to ensure the quality of healthcare, treatment, and responses. This study aimed to evaluate the cost of laboratory tests performed for patients undergoing chemotherapy treatment in the oncology treatment center at Johns Hopkins Aramco Healthcare in Saudi Arabia. Additionally, we aimed to reduce the cost of unnecessary laboratory tests in a 1-year period.

Methods

This was a quality improvement study with a quasi-experimental design using DMAIC methodology. The intervention strategy involved educating staff about adhering to the British Columbia Cancer Agency (BCCA) guidelines when ordering laboratory tests for chemotherapy patients, then integrating those guidelines into the electronic health record system. Data were collected for 200 randomly selected cases with 10 different chemotherapy protocols before and after the intervention. A paired t test was used to analyze differences in mean cost for all laboratory tests and unnecessary testing before and after the intervention.

Results

A significant cost reduction was achieved for unnecessary laboratory tests (77%, p < 0.01) when following the BCCA guidelines. In addition, the mean cost of all laboratory tests (including necessary and unnecessary) was significantly reduced by 45.5% (p = 0.023).

Conclusion

Lean thinking in clinical practice, realized by integrating a standardized laboratory test guided by BCCA guidelines into the electronic health record, significantly reduced financial costs within 1 year, thereby enhancing efficient resource utilization in the organization. This quality improvement project may serve to increase awareness of further efforts to improve resource utilization for other oncology treatment protocols.

Costs to Implement a Pediatric Weight Management Program Across 3 Distinct Contexts

BACKGROUND

The Connect for Health program is an evidence-based program that aligns with national recommendations for pediatric weight management and includes clinical decision support, educational handouts, and community resources. As implementation costs are a major driver of program adoption and maintenance decisions, we assessed the costs to implement the Connect for Health program across 3 health systems that primarily serve low-income communities with a high prevalence of childhood obesity.

METHODS

We used time-driven activity-based costing methods. Each health system (site) developed a process map and a detailed report of all implementation actions taken, aligned with major implementation requirements (eg, electronic health record integration) or strategies (eg, providing clinician training). For each action, sites identified the personnel involved and estimated the time they spent, allowing us to estimate the total costs of implementation and breakdown costs by major implementation activities.

RESULTS

Process maps indicated that the program integrated easily into well-child visits. Overall implementation costs ranged from $77,103 (Prisma Health) to $84,954 (Denver Health) to $142,721 (Massachusetts General Hospital). Across implementation activities, setting up the technological aspects of the program was a major driver of costs. Other cost drivers included training, engaging stakeholders, and audit and feedback activities, though there was variability across systems based on organizational context and implementation choices.

CONCLUSIONS

Our work highlights the major cost drivers of implementing the Connect for Health program. Accounting for context-specific considerations when assessing the costs of implementation is crucial, especially to facilitate accurate projections of implementation costs in future settings.

Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care

This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.

Cost of Implementing an Evidence-Based Intervention to Support Safer Use of Antipsychotics in Youth

To estimate the cost of implementing a clinical program designed to support safer use of antipsychotics in children and adolescents (youth) age 3-17 years at the time of initiating an antipsychotic medication. We calculate the costs of implementing a psychiatric consultation and navigation program for youth prescribed antipsychotic medications across 4 health systems, which included an electronic health record (EHR) decision support tool, consultation with a child and adolescent psychiatrist, and up to 6 months of behavioral health care navigation, as well as telemental health for patients (n = 348). Cost data were collected for both start-up and ongoing intervention phases and are estimated over a 1-year period. Data sources included study records and time-in-motion reports, analyzed from a health system perspective. Costs included both labor and nonlabor costs (2019 US dollars). The average total start-up and ongoing costs per health system were $34,007 and $185,174, respectively. The average total cost per patient was $2,128. The highest average ongoing labor cost components were telemental health ($901 per patient), followed by child and adolescent psychiatrist consultation ($659), and the lowest cost component was primary care/behavioral health provider time to review/respond to the EHR decision support tool and case consultation ($24). For health systems considering programs to promote safer and targeted use of antipsychotics among youth, this study provides estimates of the full start-up and ongoing costs of an EHR decision support tool, psychiatric consultation service, and psychotherapeutic services for patients and families.Trial registration: Clinicaltrials.gov, NCT03448575.

Clinical decision support and electronic interventions to improve care quality in chronic liver diseases and cirrhosis

Significant quality gaps exist in the management of chronic liver diseases and cirrhosis. Clinical decision support systems-information-driven tools based in and launched from the electronic health record-are attractive and potentially scalable prospective interventions that could help standardize clinical care in hepatology. Yet, clinical decision support systems have had a mixed record in clinical medicine due to issues with interoperability and compatibility with clinical workflows. In this review, we discuss the conceptual origins of clinical decision support systems, existing applications in liver diseases, issues and challenges with implementation, and emerging strategies to improve their integration in hepatology care.

Evaluating the costs and consequences of computerized clinical decision support systems in hospitals: a scoping review and recommendations for future practice

OBJECTIVE

Sustainable investment in computerized decision support systems (CDSS) requires robust evaluation of their economic impacts compared with current clinical workflows. We reviewed current approaches used to evaluate the costs and consequences of CDSS in hospital settings and presented recommendations to improve the generalizability of future evaluations.

MATERIALS AND METHODS

A scoping review of peer-reviewed research articles published since 2010. Searches were completed in the PubMed, Ovid Medline, Embase, and Scopus databases (last searched February 14, 2023). All studies reported the costs and consequences of a CDSS-based intervention compared with current hospital workflows. Findings were summarized using narrative synthesis. Individual studies were further appraised against the Consolidated Health Economic Evaluation and Reporting (CHEERS) 2022 checklist.

RESULTS

Twenty-nine studies published since 2010 were included. Studies evaluated CDSS for adverse event surveillance (5 studies), antimicrobial stewardship (4 studies), blood product management (8 studies), laboratory testing (7 studies), and medication safety (5 studies). All studies evaluated costs from a hospital perspective but varied based on the valuation of resources affected by CDSS implementation, and the measurement of consequences. We recommend future studies follow guidance from the CHEERS checklist; use study designs that adjust for confounders; consider both the costs of CDSS implementation and adherence; evaluate consequences that are directly or indirectly affected by CDSS-initiated behavior change; examine the impacts of uncertainty and differences in outcomes across patient subgroups.

DISCUSSION AND CONCLUSION

Improving consistency in the conduct and reporting of evaluations will enable detailed comparisons between promising initiatives, and their subsequent uptake by decision-makers.

Hierarchical Two-Stage Cost-Sensitive Clinical Decision Support System for Screening Prodromal Alzheimer's Disease and Related Dementias

BACKGROUND

The detection of subtle cognitive impairment in a clinical setting is difficult. Because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal.

OBJECTIVE

In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system.

METHODS

The best primary stage incorporated measures of short-term memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1,181 ADNI participants and tested on 127 patients from a memory clinic.

RESULTS

The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and diagnostic odds ratio of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression and gradient-boosted machine.

CONCLUSION

The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm.

Residents' Reflections on Cost-Conscious Care after International Health Electives: A Single-Center Qualitative Study

BACKGROUND

Estimates suggest 30% of health care expenditures are wasteful. This has led to increased educational interventions in graduate medical education (GME) training aimed to prepare residents for high value, cost-conscious practice. International health electives (IHE) are widely available in GME training and may be provide trainees a unique perspective on principles related to high value, cost-conscious care (HVCCC).

OBJECTIVE

The purpose of this study was to explore how trainee reflections on IHE experiences offer insight into HVCCC.

DESIGN

The authors conducted an applied thematic analysis of narrative reflective reports of GME trainees' IHE experiences to characterize their perceptions of HVCCC.

PARTICIPANTS

The Mayo International Health Program (MIHP) supports residents and fellows from all specialties across all Mayo Clinic sites. We included 546 MIHP participants from 2001 to 2020.

APPROACH

The authors collected post-elective narrative reports from all MIHP participants. Reflections were coded and themes were organized into model for transformative learning during IHEs, focusing on HVCCC.

KEY RESULTS

GME trainees across 24 different medical specialties participated in IHEs in 73 different countries. Three components of transformative learning were identified: disorienting dilemma, critical reflection, and commitment to behavior change. Within the component of critical reflection, three topics related to HVCCC were identified: cost transparency, resource stewardship, and reduced fear of litigation. Transformation was demonstrated through reflection on future behavioral change, including cost-aware practice, stepwise approach to health care, and greater reliance on clinical skills.

CONCLUSIONS

IHEs provide rich experiences for transformative learning and reflection on HVCCC. These experiences may help shape trainees' ideology of and commitment to HVCCC practices.

Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis

Objectives

Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.

Material and Methods

We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted.

Results

The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY.

Conclusion

We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies.

Registration

PROSPERO (CRD42020203716)

Evaluating Uptake of Evidence-Based Interventions in 355 Clinics Partnering With the Colorectal Cancer Control Program, 2015-2018

PURPOSE AND OBJECTIVES

Colorectal cancer screening rates remain suboptimal in the US. The Colorectal Cancer Control Program (CRCCP) of the Centers for Disease Control and Prevention (CDC) seeks to increase screening in health system clinics through implementation of evidence-based interventions (EBIs) and supporting activities (SAs). This program provided an opportunity to assess the uptake of EBIs and SAs in 355 clinics that participated from 2015 to 2018.

INTERVENTION APPROACH

The 30 funded awardees of CRCCP partnered with clinics to implement at least 2 of 4 EBIs that CDC prioritized (patient reminders, provider reminders, reducing structural barriers, provider assessment and feedback) and 4 optional strategies that CDC identified as SAs (small media, professional development and provider education, patient navigation, and community health workers).

EVALUATION METHODS

Clinics completed 3 annual surveys to report uptake, implementation, and integration and perceived sustainability of the priority EBIs and SAs.

RESULTS

In our sample of 355 clinics, uptake of 4 EBIs and 2 SAs significantly increased over time. By year 3, 82% of clinics implemented patient reminder systems, 88% implemented provider reminder systems, 82% implemented provider assessment and feedback, 76% implemented activities to reduce structural barriers, 51% implemented provider education, and 84% used small media. Most clinics that implemented these strategies (>90%) considered them fully integrated into the health system or clinic operations and sustainable by year 3. Fewer clinics used patient navigation (30%) and community health workers (19%), with no increase over the years of the study.

IMPLICATIONS FOR PUBLIC HEALTH

Clinics participating in the CRCCP reported high uptake and perceived sustainability of EBIs that can be integrated into electronic medical record systems but limited uptake of patient navigation and community health workers, which are uniquely suited to reduce cancer disparities. Future research should determine how to promote uptake and assess cost-effectiveness of CRCCP interventions.

E-Health Decision Support Technologies in the Prevention and Management of Pressure Ulcers: A Systematic Review

Pressure ulcers are problematic across clinical settings, negatively impacting patient morbidity and mortality while resulting in substantial costs to the healthcare system. E-health clinical decision support technologies can play a key role in improving pressure ulcer-related outcomes. This systematic review aimed to assess the impact of electronic health decision support interventions on pressure ulcer management and prevention. A systematic search was conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, and Cochrane. Nineteen articles, published from 2010 to 2020, were included for review. The findings of this review showed promising results regarding the usability and accuracy of electronic health decision support tools to aid in pressure ulcer prevention and management. Evidence indicated improved clinician adherence to pressure ulcer prevention practices and decreased healthcare costs postimplementation of an electronic health decision support intervention. However, the studies included in this review did not consistently show reductions in pressure ulcer prevalence, incidence, or risk. Most of the articles included in the review were limited by small sample sizes drawn from single hospitals or long-term care homes. More high-quality studies are needed to determine the types of electronic health decision support tools that can drive sustainable improvements to patient outcomes.

Supporting deprescribing in hospitalised patients: formative usability testing of a computerised decision support tool

Background

Despite 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.

Methods

Our 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.

Results

Participants 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.

Conclusion

Usability 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01484-z.