Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation

Optimising a clinical decision support tool to improve chronic kidney disease management in general practice

BACKGROUND

Early identification and treatment of chronic disease is associated with better clinical outcomes, lower costs, and reduced hospitalisation. Primary care is ideally placed to identify patients at risk of, or in the early stages of, chronic disease and to implement prevention and early intervention measures. This paper evaluates the implementation of a technological intervention called Future Health Today that integrates with general practice EMRs to (1) identify patients at-risk of, or with undiagnosed or untreated, chronic kidney disease (CKD), and (2) provide guideline concordant recommendations for patient care. The evaluation aimed to identify the barriers and facilitators to successful implementation.

METHODS

Future Health Today was implemented in 12 general practices in Victoria, Australia. Fifty-two interviews with 30 practice staff were undertaken between July 2020 and April 2021. Practice characteristics were collected directly from practices via survey. Data were analysed using inductive and deductive qualitative analysis strategies, using Clinical Performance - Feedback Intervention Theory (CP-FIT) for theoretical guidance.

RESULTS

Future Health Today was acceptable, user friendly and useful to general practice staff, and supported clinical performance improvement in the identification and management of chronic kidney disease. CP-FIT variables supporting use of FHT included the simplicity of design and delivery of actionable feedback via FHT, good fit within existing workflow, strong engagement with practices and positive attitudes toward FHT. Context variables provided the main barriers to use and were largely situated in the external context of practices (including pressures arising from the COVID-19 pandemic) and technical glitches impacting installation and early use. Participants primarily utilised the point of care prompt rather than the patient management dashboard due to its continued presence, and immediacy and relevance of the recommendations on the prompt, suggesting mechanisms of compatibility, complexity, actionability and credibility influenced use. Most practices continued using FHT after the evaluation phase was complete.

CONCLUSIONS

This study demonstrates that FHT is a useful and acceptable software platform that provides direct support to general practice in identifying and managing patients with CKD. Further research is underway to explore the effectiveness of FHT, and to expand the conditions on the platform.

Development and Validation of a Treatment Algorithm for Osteoarthritis Pain Management in Patients With End-Stage Kidney Disease Undergoing Hemodialysis

Background

Although osteoarthritis is common in the hemodialysis population and leads to poor health outcomes, pain management is challenged by the absence of clinical guidance. A treatment algorithm was developed and validated to aid hemodialysis clinicians in managing osteoarthritis pain.

Objective

The objective was to develop and validate a treatment algorithm for managing osteoarthritis pain in patients undergoing hemodialysis.

Design

A validation study was conducted based on Lynn's method for content validation.

Setting

To develop and validate a treatment algorithm, interviews were conducted virtually by the primary researcher with clinicians from various institutions across the Greater Toronto and Hamilton Area in Ontario.

Patients

The treatment algorithm was developed and validated for the management of osteoarthritis pain in patients on hemodialysis. Patients were not involved in the development or validation of the tool.

Measurements

The algorithm was measured for content and face validity. Content validity was measured by calculating the content validity index of each component (I-CVI) of the algorithm and the overall scale validity index (S-CVI). Face validity was assessed by calculating the percentage of positive responses to the face validity statements.

Methods

A draft algorithm was developed based on literature searches and expert opinion and validated by interviewing nephrology and pain management clinicians. Through consecutive rounds of 1:1 interviews, content and face validity were assessed by asking participants to rate the relevance of each component of the algorithm and indicate their level of agreeability with a series of statements. Following each round, the I-CVI of the algorithm as well as the S-CVI was calculated and the percentage of positive responses to the statements was determined. The research team revised the algorithm in response to the findings. The final algorithm provides a stepwise approach to the non-pharmacologic and pharmacologic management of pain, including topical, oral, and opioid use.

Results

A total of 18 clinicians from 7 institutions across the Greater Toronto and Hamilton Area were interviewed (10 pharmacists, 5 nurse practitioners, and 3 physicians). The average S-CVI of the algorithm across all 3 rounds was 0.93. At least 78% of participants provided positive responses to the face validity statements.

Limitations

An algorithm was developed based on input from clinicians working in the province of Ontario, limiting the generalizability of the algorithm across provinces. In addition, the algorithm did not include the perspectives of primary care providers or patients/caregivers.

Conclusions

An algorithm for the management of osteoarthritis pain in the hemodialysis population was developed and validated through expert review to standardize practices and encourage clinicians to use evidence-based treatments and address the psychosocial symptoms of pain. As the algorithm possesses a high degree of content and face validity, it may improve osteoarthritis pain management among patients undergoing hemodialysis. Future research will assess the implementation of the algorithm across hemodialysis settings.

Optimising diagnosis and management of kidney disease: an implementation trial of a clinical decision support system future health today

BACKGROUND

Chronic kidney disease affects more than 10% of the world's population and is a non-communicable disease of global concern and priority. There is a significant implementation gap between best practice guideline recommendations and current kidney disease management. Previous research has shown the need to partner with primary care to improve education, collaboration, and kidney disease awareness. This implementation trial will explore use of an innovative clinical decision support software, Future Health Today, to improve screening, diagnosis, and management of kidney disease in primary care. The program will be supported by tertiary care outreach services. The primary aim is to test the hypothesis that the Future Health Today implementation program will improve screening, diagnosis, and management of kidney disease. Secondary aims are to evaluate primary care satisfaction and broader health service impacts.

METHODS

This pre-post implementation trial using an interrupted time series design will evaluate the clinical and service outcomes of Future Health Today, using a mixed methods study in twenty general practices with an estimated population size of 150,000. Deidentified patient data will be extracted from participating practices to examine the primary aims of the study. Surveys and semi-structured interviews with general practice will inform secondary hypotheses. Data linkage between primary care and tertiary care data will examine the broader health service impacts.

DISCUSSION

This investigator driven trial will assess the impact of Future Health Today software coupled with education and clinical outreach support. Investigators hypothesise that there will be improvement in appropriate screening, diagnosis, and management of kidney disease. This program has the potential to be scaled more broadly.

TRIAL REGISTRATION

Australian New Zealand Clinical Trial Registry: ACTRN12623001096640.

Clinical Decision Support Tools in the Electronic Medical Record

The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.

Examining primary care provider experiences with using a clinical decision support tool for pain management

Objective

To evaluate primary care provider (PCP) experiences using a clinical decision support (CDS) tool over 16 months following a user-centered design process and implementation.

Materials and Methods

We conducted a qualitative evaluation of the Chronic Pain OneSheet (OneSheet), a chronic pain CDS tool. OneSheet provides pain- and opioid-related risks, benefits, and treatment information for patients with chronic pain to PCPs. Using the 5 Rights of CDS framework, we conducted and analyzed semi-structured interviews with 19 PCPs across 2 academic health systems.

Results

PCPs stated that OneSheet mostly contained the right information required to treat patients with chronic pain and was correctly located in the electronic health record. PCPs used OneSheet for distinct subgroups of patients with chronic pain, including patients prescribed opioids, with poorly controlled pain, or new to a provider or clinic. PCPs reported variable workflow integration and selective use of certain OneSheet features driven by their preferences and patient population. PCPs recommended broadening OneSheet access to clinical staff and patients for data entry to address clinician time constraints.

Discussion

Differences in patient subpopulations and workflow preferences had an outsized effect on CDS tool use even when the CDS contained the right information identified in a user-centered design process.

Conclusions

To increase adoption and use, CDS design and implementation processes may benefit from increased tailoring that accommodates variation and dynamics among patients, visits, and providers.

Clinical decision support in promoting evidence-based nursing in primary healthcare: a cross-sectional study in Finland

INTRODUCTION AND AIMS

The aim was to explore clinical decision support (CDS) use in the practice of primary healthcare nurses. The objectives were to recognize to what extent nurses (registered nurses, public health nurses, and practical nurses) use CDS, what factors were associated with the CDS used, what kind of organizational support nurses need, and what were nurses' views about CDS development needs.

METHODS

The study was conducted with a cross-sectional study design, using an electronic questionnaire developed for this purpose. The questionnaire contained 14 structured questions and nine open-ended questions. The sample consisted of randomly selected primary healthcare organizations ( N  = 19) in Finland. Quantitative data were analyzed using cross-tabulation and Pearson's chi-squared test, and qualitative data with quantification.

RESULTS

A total of 267 healthcare professionals (age range 22-63 years) volunteered to participate. Participants were mainly registered nurses, public health nurses, and practical nurses (46.8, 24, and 22.9%, respectively). Overall, 59% of the participants had never used CDS. The majority (92%) found it necessary to develop nursing-specific content for CDS. The most commonly used features were medication recommendations and warnings (74%), reminders (56%), and calculators (42%). Half of the participants (51%) had not received training on the use of CDS. The older age of participants was associated with the feeling of not having enough training to use CDS ( P  = 0.039104). Nurses felt that CDS was helpful in their clinical work and decision-making, promoting evidence-based practice, and narrowing the research-into-practice gap, improving patient safety and the quality of care, and helping those who are new in their work.

CONCLUSION

CDS and its support structures should be developed from a nursing perspective to achieve the full potential of CDS in nursing practice.

Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol

INTRODUCTION

Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.

METHODS AND ANALYSIS

This scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.

ETHICS AND DISSEMINATION

Ethics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease.

Chronic disease IMPACT (chronic disease early detection and improved management in primary care project): An Australian stepped wedge cluster randomised trial

Background

Interrelated chronic vascular diseases (chronic kidney disease (CKD), type 2 diabetes (T2D) and cardiovascular disease (CVD)) are common with high morbidity and mortality. This study aimed to assess if an electronic-technology-based quality improvement intervention in primary care could improve detection and management of people with and at risk of these diseases.

Methods

Stepped-wedge trial with practices randomised to commence intervention in one of five 16-week periods. Intervention included (1) electronic-technology tool extracting data from general practice electronic medical records and generating graphs and lists for audit; (2) education regarding chronic disease and the electronic-technology tool; (3) assistance with quality improvement audit plan development, benchmarking, monitoring and support. De-identified data analysis using R 3.5.1 conducted using Bayesian generalised linear mixed model with practice and time-specific random intercepts.

Results

At baseline, eight included practices had 37,946 active patients (attending practice ≥3 times within 2 years) aged ≥18 years. Intervention was associated with increased OR (95% CI) for: kidney health checks (estimated glomerular filtration rate, urine albumin:creatinine ratio (uACR) and blood pressure) in those at risk 1.34 (1.26-1.42); coded diagnosis of CKD 1.18 (1.09-1.27); T2D diagnostic testing (fasting glucose or HbA1c) in those at risk 1.15 (1.08-1.23); uACR in patients with T2D 1.78 (1.56-2.05). Documented eye checks within recommended frequency in patients with T2D decreased 0.85 (0.77-0.96). There were no significant changes in other assessed variables.

Conclusions

This electronic-technology-based intervention in primary care has potential to help translate guidelines into practice but requires further refining to achieve widespread improvements across the interrelated chronic vascular diseases.

Proceedings from the Second Annual Conference of the Norwegian Network for Implementation Research

In 2019, Norwegian implementation researchers formed a network to promote implementation research and practice in the Norwegian context. On November 19th, 2021, the second annual Norwegian implementation conference was held in Oslo. Ninety participants from all regions of the country gathered to showcase the frontiers of Norwegian implementation research. The conference also hosted a panel discussion about critical next steps for implementation science in Norway. The conference included 17 presentations from diverse disciplines within health and welfare services, including schools. The themes presented included stakeholder engagement, implementation mechanisms, evaluations of the implementation of specific interventions, the use of implementation guidelines and frameworks, the development and validation of implementation measurements, and barriers and facilitators for implementation. The panel discussion highlighted several critical challenges with the implementation of evidence-informed practices in Norway, including limited implementation competence and capacity among practice leaders and workforces, few opportunities for education in implementation science, limited implementation research in the Norwegian context, scarce funding possibilities for implementation research, and a lack of long-term perspectives on implementation processes. Overall, the 2021 Norwegian implementation conference showed an encouraging sign of a maturing field of science in Norway. The more voluminous proceedings from the 2020 conference called for several important advancements to improve implementation science and practice in Norway, and the 2021 conference indicates that steps have already been taken in favorable directions in terms of, for instance, research designs and measurements. However, there are still unexploited potentials for improvements in implementation research, funding, policies, and practice. Norwegian implementation researcher should be mindful of the challenges and potential pitfalls implementation science currently face as a scientific discipline.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43477-022-00069-w.