Effect of genetics clinical decision support tools on health-care providers’ decision making: a mixed-methods systematic review

Evaluation of the Accuracy of the Smart Work Injury Management (SWIM) System to Assist Case Managers in Predicting the Work Disability of Injured Workers

PURPOSE

Many countries have developed clinical decision-making support tools, such as the smart work injury management (SWIM) system in Hong Kong, to predict rehabilitation paths and address global issues related to work injury disability. This study aims to evaluate the accuracy of SWIM by comparing its predictions on real work injury cases to those made by human case managers, specifically with regard to the duration of sick leave and the percentage of permanent disability.

METHODS

The study analyzed a total of 442 work injury cases covering the period from 2012 to 2020, dividing them into non-litigated and litigated cases. The Kruskal-Wallis post hoc test with Bonferroni adjustment was used to evaluate the differences between the actual data, the SWIM predictions, and the estimations made by three case managers. The intra-class correlation coefficient was used to assess the inter-rater reliability of the case managers.

RESULTS

The study discovered that the predictions made by the SWIM model and a case manager possessing approximately 4 years of experience in case management exhibited moderate reliability in non-litigated cases. Nevertheless, there was no resemblance between SWIM's predictions regarding the percentage of permanent disability and those made by case managers.

CONCLUSION

The findings indicate that SWIM is capable of replicating the sick leave estimations made by a case manager with an estimated 4 years of case management experience, albeit with limitations in generalizability owing to the small sample size of case managers involved in the study.

IMPLICATIONS

These findings represent a significant advancement in enhancing the accuracy of CDMS for work injury cases in Hong Kong, signaling progress in the field.

Next-generation nephrology: part 2-mainstreaming genomics in nephrology, a global perspective

Kidney genetic services are being created worldwide, revolutionising the way in which we manage families with suspected monogenic kidney disease. There is potential to learn from one another, whether one is just embarking on this journey or within an established kidney genetics service model with aspirations to optimise it further. This concluding portion of our two-part educational review explores the global efforts to integrate genomics into nephrology. We discuss key considerations for establishing kidney genetics services and share insights from successful implementation in Australia, India, the United Kingdom (UK) and the United States (US), through case studies. Widespread integration of genomics within nephrology still faces barriers including limited genomics education among clinicians, high costs and ethical concerns. Educational strategies including workshop-based, online resources and clinical decision tools are aiming to address the genomic literacy gap among nephrologists. Multidisciplinary kidney genetics clinic models comprising nephrologists, geneticists, clinical scientists and counsellors are proving to be an effective model of delivering this diagnostic tool. Data of how kidney genetics clinics can foster collaboration with registries to facilitate research and shared learning to optimise care for patients are becoming evident. We also explore the importance of equitable access to genomics services across diverse populations, advocating for policies that address disparities in access to healthcare and genetic data representation. We hope to highlight the importance of upskilling the nephrology workforce to fully leverage the advances in genomic medicine and ensure comprehensive, accessible and personalised care for patients with genetic kidney diseases.

Development and Assessment of a Point-of-Care Application (Genomic Medicine Guidance) for Heritable Thoracic Aortic Disease

Background

Genetic testing can determine familial and personal risks for heritable thoracic aortic aneurysms and dissections (TAD). The 2022 American College of Cardiology/American Heart Association guidelines for TAD recommend management decisions based on the specific gene mutation. However, many clinicians lack sufficient comfort or insight to integrate genetic information into clinical practice.

Objective

We therefore developed the Genomic Medicine Guidance (GMG) application, an interactive point-of-care tool to inform clinicians and patients about TAD diagnosis, treatment, and surveillance. GMG is a REDCap-based application that combines publicly available genetic data and clinical recommendations based on the TAD guidelines into one translational education tool.

Methods

TAD genetic information in GMG was sourced from the Montalcino Aortic Consortium, a worldwide collaboration of TAD centers of excellence, and the National Institutes of Health genetic repositories ClinVar and ClinGen.

Results

The application streamlines data on the 13 most frequently mutated TAD genes with 2286 unique pathogenic mutations that cause TAD so that users receive comprehensive recommendations for diagnostic testing, imaging, surveillance, medical therapy, and preventative surgical repair, as well as guidance for exercise safety and management during pregnancy. The application output can be displayed in a clinician view or exported as an informative pamphlet in a patient-friendly format.

Conclusions

The overall goal of the GMG application is to make genomic medicine more accessible to clinicians and patients while serving as a unifying platform for research. We anticipate that these features will be catalysts for collaborative projects aiming to understand the spectrum of genetic variants contributing to TAD.

Genomic Medicine Guidance: A Point-of-Care App for Heritable Thoracic Aortic Diseases

Genetic testing can determine familial and personal risks for heritable thoracic aortic aneurysms and dissections (TAD). The 2022 ACC/AHA guidelines for TAD recommend management decisions based on the specific gene mutation. However, many clinicians lack sufficient comfort or insight to integrate genetic information into clinical practice. We therefore developed the Genomic Medicine Guidance (GMG) app, an interactive point-of care tool to inform clinicians and patients about TAD diagnosis, treatment, and surveillance. GMG is a REDCap-based app that combines publicly available genetic data and clinical recommendations based on the TAD guidelines into one translational education tool. TAD genetic information in GMG was sourced from the Montalcino Aortic Consortium, a worldwide collaboration of TAD centers of excellence, and the NIH genetic repositories ClinVar and ClinGen. The app streamlines data on the 13 most frequently mutated TAD genes with 2,286 unique pathogenic mutations that cause TAD so that users receive comprehensive recommendations for diagnostic testing, imaging, surveillance, medical therapy, preventative surgical repair, as well as guidance for exercise safety and management during pregnancy. The app output can be displayed in a clinician view or exported as an informative pamphlet in a patient-friendly format. The overall goal of the GMG app is to make genomic medicine more accessible to clinicians and patients, while serving as a unifying platform for research. We anticipate that these features will be catalysts for collaborative projects that aim to understand the spectrum of genetic variants that contribute to TAD.

Development of an Electronic Health Record-Based Clinical Decision Support Tool for Patients With Lynch Syndrome

PURPOSE

To develop an electronic health record (EHR)-based clinical decision support (CDS) tool to promote guideline-recommended cancer risk management among patients with Lynch syndrome (LS), an inherited cancer syndrome that confers an increased risk of colorectal and other cancer types.

MATERIALS AND METHODS

We conducted a cross-sectional study to determine the baseline prevalence and predictors of guideline-recommended colonic surveillance and annual genetics program visits among patients with LS. Multivariable log-binomial regressions estimated prevalence ratios (PRs) of cancer risk management adherence by baseline sociodemographic and clinical characteristics. These analyses provided rationale for the development of an EHR-based CDS tool to support patients and clinicians with LS-related endoscopic surveillance and annual genetics program visits. The CDS leverages an EHR platform linking discrete genetic data to LS Genomic Indicators, in turn driving downstream clinician- and patient-facing CDS.

RESULTS

Among 323 patients with LS, cross-sectional adherence to colonic surveillance and annual genetics program visits was 69.3% and 55.4%, respectively. Patients with recent electronic patient portal use were more likely to be adherent to colonic surveillance (PR, 1.67; 95% CI, 1.11 to 2.52). Patients more recently diagnosed with LS were more likely to be adherent to annual genetics program visits (PR, 0.58; 95% CI, 0.44 to 0.76 for 2-4 years; PR, 0.62; 95% CI, 0.51 to 0.75 for ≥4 compared with <2 years). Our EHR-based CDS tool is now active for 421 patients with LS throughout our health system.

CONCLUSION

We have successfully developed an EHR-based CDS tool to promote guideline-recommended cancer risk management among patients with LS.

Workforce Implications of Increased Referrals to Hereditary Cancer Services in Canada: A Scenario-Based Analysis

Over the last decade, utilization of clinical genetics services has grown rapidly, putting increasing pressure on the workforce available to deliver genetic healthcare. To highlight the policy challenges facing Canadian health systems, a needs-based workforce requirements model was developed to determine the number of Canadian patients in 2030 for whom an assessment of hereditary cancer risk would be indicated according to current standards and the numbers of genetic counsellors, clinical geneticists and other physicians with expertise in genetics needed to provide care under a diverse set of scenarios. Our model projects that by 2030, a total of 90 specialist physicians and 326 genetic counsellors (1.7-fold and 1.6-fold increases from 2020, respectively) will be required to provide Canadians with indicated hereditary cancer services if current growth trends and care models remain unchanged. However, if the expansion in eligibility for hereditary cancer assessment accelerates, the need for healthcare providers with expertise in genetics would increase dramatically unless alternative care models are widely adopted. Increasing capacity through service delivery innovation, as well as mainstreaming of cancer genetics care, will be critical to Canadian health systems' ability to meet this challenge.

Pharmacogenomic Clinical Decision Support: A Scoping Review

Clinical decision support (CDS) is often cited as an essential part of pharmacogenomics (PGx) implementations. A multitude of strategies are available; however, it is unclear which strategies are effective and which metrics are used to quantify clinical utility. The objective of this scoping review was to aggregate previous studies into a cohesive depiction of the current state of PGx CDS implementations and identify areas for future research on PGx CDS. Articles were included if they (i) described electronic CDS tools for PGx and (ii) reported metrics related to PGx CDS. Twenty of 3,449 articles were included and provided data on PGx CDS metrics from 15 institutions, with 93% of programs located at academic medical centers. The most common tools in CDS implementations were interruptive post-test alerts. Metrics for clinical response and alert response ranged from 12-73% and 21-98%, respectively. Few data were found on changes in metrics over time and measures that drove the evolution of CDS systems. Relatively few data were available regarding support of optimal approaches for PGx CDS. Post-test alerts were the most widely studied approach, and their effectiveness varied greatly. Further research on the usability, effectiveness, and optimization of CDS tools is needed.

Implementation of pharmacogenomic clinical decision support for health systems: a cost-utility analysis

We constructed a cost-effectiveness model to assess the clinical and economic value of a CDS alert program that provides pharmacogenomic (PGx) testing results, compared to no alert program in acute coronary syndrome (ACS) and atrial fibrillation (AF), from a health system perspective. We defaulted that 20% of 500,000 health-system members between the ages of 55 and 65 received PGx testing for CYP2C19 (ACS-clopidogrel) and CYP2C9, CYP4F2 and VKORC1 (AF-warfarin) annually. Clinical events, costs, and quality-adjusted life years (QALYs) were calculated over 20 years with an annual discount rate of 3%. In total, 3169 alerts would be fired. The CDS alert program would help avoid 16 major clinical events and 6 deaths for ACS; and 2 clinical events and 0.9 deaths for AF. The incremental cost-effectiveness ratio was $39,477/QALY. A PGx-CDS alert program was cost-effective, under a willingness-to-pay threshold of $100,000/QALY gained, compared to no alert program.

Precision medicine in mood disorders

The choice of the most appropriate psychoactive medication for each of our patients is always a challenge. We can use more than 100 psychoactive drugs in the treatment of mood disorders, which can be prescribed either alone or in combination. Response and tolerability problems are common, and much trial and error is often needed before achieving a satisfactory outcome. Precision medicine is therefore needed for tailoring treatment to optimize outcome. Pharmacological, clinical, and demographic factors are important and informative, but biological factors may further inform and refine prediction. Twenty years after the first reports of gene variants modulating antidepressant response, we are now confronted with the prospect of routine clinical pharmacogenetic applications in the treatment of depression. The scientific community is divided into two camps: those who are enthusiastic and those who are skeptical. Although it appears clear that the benefit of existing tools is still not completely defined, at least in the case of central nervous system gene variants, this is not the case for metabolic gene variants, which is generally accepted. Cumulative scores encompassing many variants across the entire genome will soon predict psychiatric disorder liability and outcome. At present, precision medicine in mood disorders may be implemented using clinical and pharmacokinetic factors. In the near future, a genome-wide composite genetic score in conjunction with clinical factors within each patient is the most promising approach for developing a more effective way to target treatment for patients suffering from mood disorders.

Challenges and practical solutions for managing secondary genomic findings in primary care

Primary care providers will increasingly be tasked with managing most secondary findings from genomic sequencing, but literature exploring their capacity to manage findings beyond conventional genetic testing is limited. This study aimed to explore primary care providers' challenges and potential solutions for managing secondary findings. Providers were recruited in two groups. Group 1 providers had a patient in their practice who received secondary findings and all potential group 1 providers were invited to participate. Group 2 providers were provided with the secondary findings of a hypothetical patient and were purposefully sampled for maximal variation in sex, practice setting, and geographic location. Providers were interviewed about their challenges and solutions managing secondary findings from a patient in their practice or a hypothetical patient. Using interpretive description methodology, transcripts were analysed thematically complemented by constant comparison. Out of the fifty-five providers invited, 15 family physicians participated across community and academic settings in Ontario, Canada (range 6-40 years in practice; 10/15 female). Providers described a responsibility to manage secondary findings, but limited capacity for this, describing practice, knowledge, and technical challenges. Providers expressed concern that compared to other incidental findings, secondary genomic findings might be reported directly to patients and result in longer-term anxiety. Potential solutions were a structured letter with categorized results and summary tables highlighting key secondary findings with follow-up recommendations and resources, as well as electronic medical records (EMRs) that store and integrate genomic information for prescribing or referrals. These solutions were deemed essential to address knowledge and technical challenges faced by primary care physicians and ultimately promote clinical utility of secondary findings.