Can computerized clinical decision support systems improve diabetes management? A systematic review and meta-analysis

Technology in the management of diabetes in hospitalised adults

Suboptimal glycaemic management in hospitals has been associated with adverse clinical outcomes and increased financial costs to healthcare systems. Despite the availability of guidelines for inpatient glycaemic management, implementation remains challenging because of the increasing workload of clinical staff and rising prevalence of diabetes. The development of novel and innovative technologies that support the clinical workflow and address the unmet need for effective and safe inpatient diabetes care delivery is still needed. There is robust evidence that the use of diabetes technology such as continuous glucose monitoring and closed-loop insulin delivery can improve glycaemic management in outpatient settings; however, relatively little is known of its potential benefits and application in inpatient diabetes management. Emerging data from clinical studies show that diabetes technologies such as integrated clinical decision support systems can potentially mediate safer and more efficient inpatient diabetes care, while continuous glucose sensors and closed-loop systems show early promise in improving inpatient glycaemic management. This review aims to provide an overview of current evidence related to diabetes technology use in non-critical care adult inpatient settings. We highlight existing barriers that may hinder or delay implementation, as well as strategies and opportunities to facilitate the clinical readiness of inpatient diabetes technology in the future.

Technology-supported shared decision-making in chronic conditions: A systematic review of randomized controlled trials

OBJECTIVES

To describe the role of patients with a chronic disease, healthcare professionals (HCPs) and technology in shared decision making (SDM) and the use of clinical decision support systems (CDSSs), and to evaluate the effectiveness of SDM and CDSSs interventions.

METHODS

Randomized controlled studies published between 2011 and 2021 were identified and screened independently by two reviewers, followed by data extraction and analysis. SDM elements and interactive styles were identified to shape the roles of patients, HCPs and technology.

RESULTS

Forty-three articles were identified and reported on 21 SDM-studies, 15 CDSS-studies, 2 studies containing both an SDM-tool and a CDSS, and 5 studies with other decision support components. SDM elements were mostly identified in SDM-tools and interactions styles were least common in the other decision support components.

CONCLUSIONS

Patients within the included RCTs mainly received information from SDM-tools and occasionally CDSSs when it concerns treatment strategies. HCPs provide and clarify information using SDM-tools and CDSSs. Technology provides interactions, which can support more active SDM. SDM-tools mostly showed evidence for positive effects on SDM outcomes, while CDSSs mostly demonstrated positive effects on clinical outcomes.

PRACTICE IMPLICATIONS

Technology-supported SDM has potential to optimize SDM when patients, HCPs and technology collaborate well together.

Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review

Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.

Implementing a Sodium-Glucose Cotransporter 2 Inhibitor Module With a Software Tool (Future Health Today): Qualitative Study

BACKGROUND

Primary care plays a key role in the management of type 2 diabetes. Sodium-glucose cotransporter 2 (SGLT2) inhibitors have been demonstrated to reduce hospitalization and cardiac and renal complications. Tools that optimize management, including appropriate prescribing, are a priority for treating chronic diseases. Future Health Today (FHT) is software that facilitates clinical decision support and quality improvement. FHT applies algorithms to data stored in electronic medical records in general practice to identify patients who are at risk of a chronic disease or who have a chronic disease that may benefit from intensification of management. The platform continues to evolve because of rigorous evaluation, continuous improvement, and expansion of the conditions hosted on the platform. FHT currently displays recommendations for the identification and management of chronic kidney disease, cardiovascular disease, type 2 diabetes, and cancer risk. A new module will be introduced to FHT focusing on SGLT2 inhibitors in patients with type 2 diabetes who have chronic kidney diseases, cardiovascular diseases, or risk factors for cardiovascular disease.

OBJECTIVE

The study aims to explore the barriers and enablers to the implementation of an SGLT2 inhibitor module within the Future Health Today software.

METHODS

Clinic staff were recruited to participate in interviews on their experience in their use of a tool to improve prescribing behavior for SGLT2 inhibitors. Thematic analysis was guided by Clinical Performance Feedback Intervention Theory.

RESULTS

In total, 16 interviews were completed. Identified enablers of use included workflow alignment, clinical appropriateness, and active delivery of the module. Key barriers to use were competing priorities, staff engagement, and knowledge of the clinical topic.

CONCLUSIONS

There is a recognized benefit to the use of a clinical decision support tool to support type 2 diabetes management, but barriers were identified that impeded the usability and actionability of the module. Successful and effective implementation of this tool could support the optimization of patient management of type 2 diabetes in primary care.

Clinical decision support systems for diabetic foot ulcers: a scoping review

OBJECTIVE

Map the scientific evidence on the use of clinical decision support systems in diabetic foot care.

METHOD

A scoping review based on the JBI Manual for Evidence Synthesis and registered on the Open Science Framework platform. Searches were carried out in primary and secondary sources on prototypes and computerized tools aimed at assisting patients with diabetic foot or at risk of having it, published in any language or period, in eleven databases and grey literature.

RESULTS

A total of 710 studies were identified and, following the eligibility criteria, 23 were selected, which portrayed the use of decision support systems in diabetic foot screening, predicting the risk of ulcers and amputations, classifying the stage of severity, deciding on the treatment plan, and evaluating the effectiveness of interventions, by processing data relating to clinical and sociodemographic information.

CONCLUSION

Expert systems stand out for their satisfactory results, with high precision and sensitivity when it comes to guiding and qualifying the decision-making process in diabetic foot prevention and care.

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

Implementation of a clinical decision support system for the optimization of antidiabetic drug orders by pharmacists

AIMS

The objective of the study was to describe the impact of a clinical decision support system (CDSS) on antidiabetic drug management by clinical pharmacists for hospitalized patients with T2DM.

METHODS

We performed a retrospective, single-centre study in a teaching hospital, where clinical pharmacists analysed prescriptions and issued pharmacist interventions (PIs) through a computerized physician order entry (CPOE) system. A CDSS was integrated into the pharmacists' workflow in July 2019. We analysed PIs during 2 periods of interest: one before the introduction of the CDSS (from November 2018 to April 2019, PIs issued through the CPOE alone) and one afterwards (from November 2020 to April 2021, PIs issued through the CPOE and/or the CDSS). The study covered nondiabetology wards as endocrinology, diabetes and metabolism departments were not computerized at the time of the study.

RESULTS

There were 203 PIs related to antidiabetic drugs in period 1 and 319 in period 2 (a 57.5% increase). Sixty-four of the 319 PIs were generated by the CDSS. Noncompliance/contraindication was the main problem identified by the CDSS (41 PIs, 68.4%), and 57.8% led to discontinuation of the drug. Most of the PIs issued through the CDSS corresponded to orders that had not been flagged up by clinical pharmacists using the CPOE. Conversely, most alerts about indications that were not being treated were detected by the clinical pharmacists using the CPOE and not by the CDSS.

CONCLUSION

Use of CDSS by clinical pharmacists improved antidiabetic drug management for hospitalized patients with T2DM. The CDSS might add value to diabetes care in nondiabetology wards by decreasing the frequency of potentially inappropriate prescriptions and adverse drug reactions.

Features, functions, and quality of mobile applications for type 2 diabetes care in China: Systematic search of app stores

BACKGROUND

Type 2 diabetes (T2DM) is highly prevalent in China, affecting over 114 million people. While mHealth interventions have shown promise, there is limited research on T2DM management apps in real-world app stores.

OBJECTIVE

This study aimed to systematically search and analyze T2DM care mobile apps in the Chinese market, describing their features, and functions, and evaluating the quality of the most popular apps using validated tools.

METHODS

We conducted a comprehensive search in Chinese Android and iOS app stores for T2DM management apps. We downloaded 138 eligible ones for a general review of their key features and function. We also assessed the quality of the top 20 apps from both platforms using the Mobile App Rating Scale (MARS) by both researcher and patient.

RESULTS

A total of 3524 apps were searched. 138 eligible apps were downloaded for general review and 29 popular apps were included for quality assessment. Most apps were designed for patient users (87.0 %) and developed by commercial companies (85.5 %). Common functions included blood glucose monitoring, diabetes education, integration with measuring devices, medication adherence reminders, teleconsultation services, and diabetes risk factor tracking. The researcher's evaluation yielded an average MARS score of 4.0 out of 5 for popular apps, with subscale scores of functionalities (4.5), aesthetics (4.1), engagement (3.7), and information (3.6). However, patient ratings were lower in functionality (3.5), aesthetics (3.4), and engagement (2.6), and the patient faced difficulties with information-related items. Similar trends were observed in subject quality items.

CONCLUSION

App developers should engage caregivers, and family members as target users, and involve government agencies as partners to improve T2DM management apps. Future apps should incorporate scientifically proven advanced functions to enhance their effectiveness. The quality assessment highlighted weaknesses in engagement and information and the importance of user-centric approaches in app development.

Effect of the clinical decision assessment system on clinical outcomes of delirium in hospitalized older adults: study protocol for a pair-matched, parallel, cluster randomized controlled superiority trial

BACKGROUND

Prompt recognition of delirium is the first key step in its proper management. A previous study has demonstrated that nurses' delirium screening using the usual paper version assessment tool has no effect on clinical outcomes. Clinical decision assessment systems have been demonstrated to improve patients' adherence and clinical outcomes. Therefore, We developed a clinical decision assessment system (3D-DST) based on the usual paper version (3-min diagnostic interview for CAM-defined delirium), which was developed for assessing delirium in older adults with high usability and accuracy. However, no high quality evidence exists on the effectiveness of a 3D-DST in improving outcomes of older adults compared to the usual paper version.

METHODS

A pair-matched, open-label, parallel, cluster randomized controlled superiority trial following the SPIRIT checklist. Older patients aged 65 years or older admitted to four medical wards of a geriatric hospital will be invited to participate in the study. Prior to the study, delirium prevention and treatment interventions will be delivered to nurses in both the intervention and control groups. The nurses in the intervention group will perform routine delirium assessments on the included older patients with 3D-DST, while the nurses in the control group will perform daily delirium assessments with the usual paper version. Enrolled patients will be assessed twice daily for delirium by a nurse researcher using 3D-DST. The primary outcome is delirium duration. The secondary outcomes are delirium severity, incidence of delirium, length of stay, in-hospital mortality, adherence to delirium assessment, prevention, and treatment of medical staff.

DISCUSSION

This study will incorporate the 3D-DST into clinical practice for delirium assessment. If our study will demonstrate that 3D-DST will improve adherence with delirium assessment and clinical outcomes in older patients, it will provide important evidence for the management of delirium in the future.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, Identifier: ChiCTR1900028402. https://www.chictr.org.cn/showproj.aspx?proj=47127 .

PROTOCOL VERSION

1, 29/7/22.

Practice facilitation for scale up of clinical decision support for hypertension management: study protocol for a cluster randomized control trial

BACKGROUND

Only half of patients with hypertension have adequately controlled blood pressure. Clinical decision support (CDS) has the potential to overcome barriers to delivering guideline-recommended care and improve hypertension management. However, optimal strategies for scaling CDS have not been well established, particularly in small, independent primary care practices which often lack the resources to effectively change practice routines. Practice facilitation is an implementation strategy that has been shown to support process changes. Our objective is to evaluate whether practice facilitation provided with hypertension-focused CDS can lead to improvements in blood pressure control for patients seen in small primary care practices.

METHODS/DESIGN

We will conduct a cluster randomized control trial to compare the effect of hypertension-focused CDS plus practice facilitation on BP control, as compared to CDS alone. The practice facilitation intervention will include an initial training in the CDS and a review of current guidelines along with follow-up for coaching and integration support. We will randomize 46 small primary care practices in New York City who use the same electronic health record vendor to intervention or control. All patients with hypertension seen at these practices will be included in the evaluation. We will also assess implementation of CDS in all practices and practice facilitation in the intervention group.

DISCUSSION

The results of this study will inform optimal implementation of CDS into small primary care practices, where much of care delivery occurs in the U.S. Additionally, our assessment of barriers and facilitators to implementation will support future scaling of the intervention.

CLINICALTRIALS

gov Identifier: NCT05588466.

Comparative Effectiveness of Team-Based Care With and Without a Clinical Decision Support System for Diabetes Management : A Cluster Randomized Trial

BACKGROUND

Uncontrolled hyperglycemia, hypercholesterolemia, and hypertension are common in persons with diabetes.

OBJECTIVE

To compare the effectiveness of team-based care with and without a clinical decision support system (CDSS) in controlling glycemia, lipids, and blood pressure (BP) among patients with type 2 diabetes.

DESIGN

Cluster randomized trial. (ClinicalTrials.gov: NCT02835287).

SETTING

38 community health centers in Xiamen, China.

PATIENTS

11 132 persons aged 50 years or older with uncontrolled diabetes and comorbid conditions, 5475 receiving team-based care with a CDSS and 5657 receiving team-based care alone.

INTERVENTION

Team-based care was delivered by primary care physicians, health coaches, and diabetes specialists in all centers. In addition, a computerized CDSS, which generated individualized treatment recommendations based on clinical guidelines, was implemented in 19 centers delivering team-based care with a CDSS.

MEASUREMENTS

Coprimary outcomes were mean reductions in hemoglobin A_1c (HbA_1c) level, low-density lipoprotein cholesterol (LDL-C) level, and systolic BP over 18 months and the proportion of participants with all 3 risk factors controlled at 18 months.

RESULTS

During the 18-month intervention, HbA_1c levels, LDL-C levels, and systolic BP significantly decreased by -0.9 percentage point (95% CI, -0.9 to -0.8 percentage point), -0.49 mmol/L (CI, -0.53 to -0.45 mmol/L) (-19.0 mg/dL [CI, -20.4 to -17.5 mg/dL]), and -9.1 mm Hg (CI, -9.9 to -8.3 mm Hg), respectively, in team-based care with a CDSS and by -0.6 percentage point (CI, -0.7 to -0.5 percentage point), -0.32 mmol/L (CI, -0.35 to -0.29 mmol/L) (-12.5 mg/dL [CI, -13.6 to -11.3 mg/dL]), and -7.5 mm Hg (CI, -8.4 to -6.6 mm Hg), respectively, in team-based care alone. Net differences were -0.2 percentage point (CI, -0.3 to -0.1 percentage point) for HbA_1c level, -0.17 mmol/L (CI, -0.21 to -0.12 mmol/L) (-6.5 mg/dL [CI, -8.3 to -4.6 mg/dL]) for LDL-C level, and -1.5 mm Hg (CI, -2.8 to -0.3 mm Hg) for systolic BP. The proportion of patients with controlled HbA_1c, LDL-C, and systolic BP was 16.9% (CI, 15.7% to 18.2%) in team-based care with a CDSS and 13.0% (CI, 11.7% to 14.3%) in team-based care alone.

LIMITATION

There was no usual care control, and clinical outcome assessors were unblinded; the analysis did not account for multiple comparisons.

CONCLUSION

Compared with team-based care alone, team-based care with a CDSS significantly reduced cardiovascular risk factors in patients with diabetes, but the effect was modest.

PRIMARY FUNDING SOURCE

Xiamen Municipal Health Commission.

Clinical Decision Support for Glycemic Management Reduces Hospital Length of Stay

OBJECTIVE

Dysglycemia influences hospital outcomes and resource utilization. Clinical decision support (CDS) holds promise for optimizing care by overcoming management barriers. This study assessed the impact on hospital length of stay (LOS) of an alert-based CDS tool in the electronic medical record that detected dysglycemia or inappropriate insulin use, coined as gaps in care (GIC).

RESEARCH DESIGN AND METHODS

Using a 12-month interrupted time series among hospitalized persons aged ≥18 years, our CDS tool identified GIC and, when active, provided recommendations. We compared LOS during 6-month-long active and inactive periods using linear models for repeated measures, multiple comparison adjustment, and mediation analysis.

RESULTS

Among 4,788 admissions with GIC, average LOS was shorter during the tool's active periods. LOS reductions occurred for all admissions with GIC (-5.7 h, P = 0.057), diabetes and hyperglycemia (-6.4 h, P = 0.054), stress hyperglycemia (-31.0 h, P = 0.054), patients admitted to medical services (-8.4 h, P = 0.039), and recurrent hypoglycemia (-29.1 h, P = 0.074). Subgroup analysis showed significantly shorter LOS in recurrent hypoglycemia with three events (-82.3 h, P = 0.006) and nonsignificant in two (-5.2 h, P = 0.655) and four or more (-14.8 h, P = 0.746). Among 22,395 admissions with GIC (4,788, 21%) and without GIC (17,607, 79%), LOS reduction during the active period was 1.8 h (P = 0.053). When recommendations were provided, the active tool indirectly and significantly contributed to shortening LOS through its influence on GIC events during admissions with at least one GIC (P = 0.027), diabetes and hyperglycemia (P = 0.028), and medical services (P = 0.019).

CONCLUSIONS

Use of the alert-based CDS tool to address inpatient management of dysglycemia contributed to reducing LOS, which may reduce costs and improve patient well-being.

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)

Assessing the implementation of a clinical decision support tool in primary care for diabetes prevention: a qualitative interview study using the Consolidated Framework for Implementation Science

BACKGROUND

In this paper we describe the use of the Consolidated Framework for Implementation Research (CFIR) to study implementation of a web-based, point-of-care, EHR-linked clinical decision support (CDS) tool designed to identify and provide care recommendations for adults with prediabetes (Pre-D CDS).

METHODS

As part of a large NIH-funded clinic-randomized trial, we identified a convenience sample of interview participants from 22 primary care clinics in Minnesota, North Dakota, and Wisconsin that were randomly allocated to receive or not receive a web-based EHR-integrated prediabetes CDS intervention. Participants included 11 clinicians, 6 rooming staff, and 7 nurse or clinic managers recruited by study staff to participate in telephone interviews conducted by an expert in qualitative methods. Interviews were recorded and transcribed, and data analysis was conducted using a constructivist version of grounded theory.

RESULTS

Implementing a prediabetes CDS tool into primary care clinics was useful and well received. The intervention was integrated with clinic workflows, supported primary care clinicians in clearly communicating prediabetes risk and management options with patients, and in identifying actionable care opportunities. The main barriers to CDS use were time and competing priorities. Finally, while the implementation process worked well, opportunities remain in engaging the care team more broadly in CDS use.

CONCLUSIONS

The use of CDS tools for engaging patients and providers in care improvement opportunities for prediabetes is a promising and potentially effective strategy in primary care settings. A workflow that incorporates the whole care team in the use of such tools may optimize the implementation of CDS tools like these in primary care settings. Trial registration Name of the registry: Clinicaltrial.gov.

TRIAL REGISTRATION NUMBER

NCT02759055. Date of registration: 05/03/2016. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT02759055 Prospectively registered.

Comparative effectiveness of team-based care with a clinical decision support system versus team-based care alone on cardiovascular risk reduction among patients with diabetes: Rationale and design of the D4C trial

BACKGROUND

Diabetes has become a major public health challenge worldwide, especially in low- and middle-income countries (LMICs). Uncontrolled hyperglycemia, hypertension, and dyslipidemia major risk factors for all-cause mortality and cardiovascular disease (CVD) are common in patients with diabetes in China. We propose to compare the effectiveness of team-based care plus a clinical decision support system (CDSS) with team-based care alone on glycemic, blood pressure (BP), and lipid control, and clinical CVD reduction among patients with type-2 diabetes and at high risk for CVD.

METHODS

The Diabetes Complication Control in Community Clinics (D4C) study is a cluster-randomized trial conducted among 38 community health centers in Xiamen City, China. Nineteen clinics have been randomly assigned to team-based care plus CDSS and 19 to team-based care alone. Team-based care includes primary care providers, health coaches, and diabetes specialists working collaboratively with patients to achieve shared treatment goals for CVD risk factor reduction. The CDSS integrates guideline-based treatment algorithms for glycemic, BP, and lipid control, along with a patient's medical history and insurance policy, to recommend treatment and follow-up plans. In phase 1, the co-primary outcomes are mean reduction in glycated hemoglobin (HbA1c), systolic BP (SBP), and low-density lipoprotein (LDL)-cholesterol over 18 months, and the proportion of patients with controlled HbA1c, SBP, and LDL-cholesterol at 18 months' between the 2 comparison groups. In phase 2, the primary outcome is the difference in major CVD incidence (non-fatal stroke, non-fatal myocardial infarction, hospitalized heart failure, and CVD mortality) between the 2 comparison groups. Mean reduction in HbA1c, SBP, and LDL-cholesterol levels will be simultaneously modeled for a single overall treatment effect.

CONCLUSION

The D4C trial will generate evidence on whether a CDSS will further reduce the CVD burden among patients with diabetes beyond team-based care at community clinics. If proven effective, this implementation strategy could be scaled up within primary care settings in China and other LMICs to reduce CVD incidence and mortality among patients with diabetes.

The impact of the implementation of a clinical decision support system on the quality of healthcare services in a primary care setting

Background

In July 2015, King Faisal Hospital Family Medicine clinics (KFH-FMC) successfully implemented a paperless, fully integrated, electronic healthcare system. The aim of this study is to evaluate the impact of moving to a fully integrated electronic medical record system, with clinical decision support (CDS) systems, on the quality of healthcare services in a primary care setting. We aim to evaluate the impact of CDS on clinical outcomes such as screening and diagnosis of breast and colorectal cancers, as well as the management of chronic diseases such as diabetes and hypertension, and the uptake of immunizations.

Inclusion and Exclusion Criteria

Our study included all adult patients, over the age of 18, registered in the Family Medicine clinic linked to King Faisal Hospital, seen between January 2012 and December 2018.

Design

Retrospective cohort study.

Setting

Family Medicine clinics at King Faisal Hospital (KFH-FMC).

Materials and Methods

Data were collected retrospectively from the electronic health records of all adult patients above 18 years of age, who were seen in KFH-FMC between January 2012 and December 2018. We analyzed several processes of care and a number of clinical outcomes, comparing results for the three and a half years before CDS implementation with the three and a half years after implementation. Data collected included blood pressure measurements, lipid levels, HbA1c for diabetic patients, screening tests done, including PAP smear, mammogram, fecal occult blood tests, and bone densitometry. Other data included cancer diagnoses and immunizations received.

Results

Significant increases were found in adult vaccine uptake ranging from an 11-fold increase in influenza uptake, to a 22-fold increase in pneumococcal 23 uptake. The uptake of all the cancer screening tests increased (FOB 66%, mammogram 33%, PAP smear 16%). Diagnoses of breast and colorectal cancer showed significant increases. Breast cancer diagnoses increased from 2 to 14, and colorectal cancer from 3 to 11. No significant improvement was found in chronic disease outcomes.

Discussion

The electronic health record with CDS led to significantly improved uptake of immunizations and screening tests, with earlier diagnoses of breast and colon cancer. Evidence of improvement in chronic disease outcomes is still lacking.

Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes

INTRODUCTION

Innovative approaches are needed to design robust clinical decision support (CDS) to optimize hospital glycemic management. We piloted an electronic medical record (EMR), evidence-based algorithmic CDS tool in an academic center to alert clinicians in real time about gaps in care related to inpatient glucose control and insulin utilization, and to provide management recommendations.

RESEARCH DESIGN AND METHODS

The tool was designed to identify clinical situations in need for action: (1) severe or recurrent hyperglycemia in patients with diabetes: blood glucose (BG) ≥13.88 mmol/L (250 mg/dL) at least once or BG ≥10.0 mmol/L (180 mg/dL) at least twice, respectively; (2) recurrent hyperglycemia in patients with stress hyperglycemia: BG ≥10.0 mmol/L (180 mg/dL) at least twice; (3) impending or established hypoglycemia: BG 3.9-4.4 mmol/L (70-80 mg/dL) or ≤3.9 mmol/L (70 mg/dL); and (4) inappropriate sliding scale insulin (SSI) monotherapy in recurrent hyperglycemia, or anytime in patients with type 1 diabetes. The EMR CDS was active (ON) for 6 months for all adult hospital patients and inactive (OFF) for 6 months. We prospectively identified and compared gaps in care between ON and OFF periods.

RESULTS

When active, the hospital CDS tool significantly reduced events of recurrent hyperglycemia in patients with type 1 and type 2 diabetes (3342 vs 3701, OR=0.88, p=0.050) and in patients with stress hyperglycemia (288 vs 506, OR=0.60, p<0.001). Hypoglycemia or impending hypoglycemia (1548 vs 1349, OR=1.15, p=0.050) were unrelated to the CDS tool on subsequent analysis. Inappropriate use of SSI monotherapy in type 1 diabetes (10 vs 22, OR=0.36, p=0.073), inappropriate use of SSI monotherapy in type 2 diabetes (2519 vs 2748, OR=0.97, p=0.632), and in stress hyperglycemia subjects (1617 vs 1488, OR=1.30, p<0.001) were recognized.

CONCLUSION

EMR CDS was successful in reducing hyperglycemic events among hospitalized patients with dysglycemia and diabetes, and inappropriate insulin use in patients with type 1 diabetes.

Guiding Glucose Management Discussions Among Adults With Type 2 Diabetes in General Practice: Development and Pretesting of a Clinical Decision Support Tool Prototype Embedded in an Electronic Medical Record

BACKGROUND

Managing type 2 diabetes (T2D) requires progressive lifestyle changes and, sometimes, pharmacological treatment intensification. General practitioners (GPs) are integral to this process but can find pharmacological treatment intensification challenging because of the complexity of continually emerging treatment options.

OBJECTIVE

This study aimed to use a co-design method to develop and pretest a clinical decision support (CDS) tool prototype (GlycASSIST) embedded within an electronic medical record, which uses evidence-based guidelines to provide GPs and people with T2D with recommendations for setting glycated hemoglobin (HbA1c) targets and intensifying treatment together in real time in consultations.

METHODS

The literature on T2D-related CDS tools informed the initial GlycASSIST design. A two-part co-design method was then used. Initial feedback was sought via interviews and focus groups with clinicians (4 GPs, 5 endocrinologists, and 3 diabetes educators) and 6 people with T2D. Following refinements, 8 GPs participated in mock consultations in which they had access to GlycASSIST. Six people with T2D viewed a similar mock consultation. Participants provided feedback on the functionality of GlycASSIST and its role in supporting shared decision making (SDM) and treatment intensification.

RESULTS

Clinicians and people with T2D believed that GlycASSIST could support SDM (although this was not always observed in the mock consultations) and individualized treatment intensification. They recommended that GlycASSIST includes less information while maintaining relevance and credibility and using graphs and colors to enhance visual appeal. Maintaining clinical autonomy was important to GPs, as they wanted the capacity to override GlycASSIST's recommendations when appropriate. Clinicians requested easier screen navigation and greater prescribing guidance and capabilities.

CONCLUSIONS

GlycASSIST was perceived to achieve its purpose of facilitating treatment intensification and was acceptable to people with T2D and GPs. The GlycASSIST prototype is being refined based on these findings to prepare for quantitative evaluation.

The effects of clinical decision support systems on insulin use: A systematic review

BACKGROUND

A clinical decision support system (CDSS) is a computerized system using case-based reasoning to assist clinicians in assessing disease status, in selecting appropriate therapy or in making other clinical decisions. Previous randomized controlled trials (RCTs or trials) have shown that CDSSs have the potential to improve the insulin use, but the evidence was conflicting and uncertain. The purpose of our study was to determine whether a CDSS improves the use of insulin.

METHOD

PubMed, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched from their inception to October 2018. The quality assessment was based on the risk of bias criteria of the Cochrane Handbook.

RESULTS

Twenty-four RCTs, involving 7653 participants, were included. Thirteen of those trials (54.2%) used a computerized algorithm or a computer-assisted insulin protocol for insulin dose and therapy adjustment, of which 30.8% (four of 13) found significant changes. Of 10 trials that measured mean blood glucose levels and the 11 trials reported HbA1c, the computerized insulin dose adjustment resulted in lower mean blood glucose levels in 70.0% (seven of 10) and 36.4% (four of 11) of RCTs, respectively. Additionally, a significant reduction of hyperglycaemia events was reported in three of six RCTs. The evidence in a majority of the 24 RCTs was of moderate quality.

CONCLUSIONS

CDSSs have the potential to improve the insulin use and blood glucose control in a clinical setting. The methodologies in these studies were of mixed quality. Better designed and longer-term studies are required to ensure a larger and more reliable evidence base on the effects of CDSS intervention on insulin use.

Supporting care for suboptimally controlled type 2 diabetes mellitus in general practice with a clinical decision support system: a mixed methods pilot cluster randomised trial

OBJECTIVES

We developed a complex intervention called DECIDE (ComputeriseD dECisIonal support for suboptimally controlleD typE 2 Diabetes mellitus in Irish General Practice) which used a clinical decision support system to address clinical inertia and support general practitioner (GP) intensification of treatment for adults with suboptimally controlled type2 diabetes mellitus (T2DM). The current study explored the feasibility and potential impact of DECIDE.

DESIGN

A pilot cluster randomised controlled trial.

SETTING

Conducted in 14 practices in Irish General Practice.

PARTICIPANTS

The DECIDE intervention was targeted at GPs. They applied DECIDE to patients with suboptimally controlled T2DM, defined as a glycated haemoglobin (HbA1c) ≥70 mmol/mol and/or blood pressure ≥150/95 mmHg.

INTERVENTION

The intervention incorporated training and a web-based clinical decision support system which supported; (i) medication intensification actions; and (ii) non-pharmacological actions to support care. Control practices delivered usual care.

PRIMARY AND SECONDARY OUTCOME MEASURES

Feasibility and acceptability was determined using thematic analysis of semi-structured interviews with GPs, combined with data from the DECIDE website. Clinical outcomes included HbA1c, medication intensification, blood pressure and lipids.

RESULTS

We recruited 14 practices and 134 patients. At 4-month follow-up, all practices and 114 patients were followed up. GPs reported finding decision support helpful navigating increasingly complex medication algorithms. However, the majority of GPs believed that the target patient group had poor engagement with GP and hospital services for a range of reasons. At follow-up, there was no difference in glycaemic control (-3.6 mmol/mol (95% CI -11.2 to 4.0)) between intervention and control groups or in secondary outcomes including, blood pressure, total cholesterol, medication intensification or utilisation of services. Continuation criteria supported proceeding to a definitive randomised trial with some modifications.

CONCLUSION

The DECIDE study was feasible and acceptable to GPs but wider impacts on glycaemic and blood pressure control need to be considered for this patient population going forward.

TRIAL REGISTRATION NUMBER

ISRCTN69498919.

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

Background

The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care.

Methods

A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate.

Results

Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was − 0.09 (95% CI − 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI − 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI − 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI − 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system.

Conclusions

The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system.

Trial registration

ClinicalTrials.gov, NCT01830569, Registered 12 April 2013.

Unintended adverse consequences of a clinical decision support system: two cases

Many institutions have implemented clinical decision support systems (CDSSs). While CDSS research papers have focused on benefits of these systems, there is a smaller body of literature showing that CDSSs may also produce unintended adverse consequences (UACs). Detailed here are 2 cases of UACs resulting from a CDSS. Both of these cases were related to external systems that fed data into the CDSS. In the first case, lack of knowledge of data categorization in an external pharmacy system produced a UAC; in the second case, the change of a clinical laboratory instrument produced the UAC. CDSSs rely on data from many external systems. These systems are dynamic and may have changes in hardware, software, vendors, or processes. Such changes can affect the accuracy of CDSSs. These cases point to the need for the CDSS team to be familiar with these external systems. This team (manager and alert builders) should include members in specific clinical specialties with deep knowledge of these external systems.

Challenges with Patient Adoption of Automated Integration of Blood Glucose Meter Data in the Electronic Health Record

Providers often encourage patients with type 1 diabetes (T1D) to contact them with blood glucose (BG) values between visits. However, patients and families find it cumbersome to share their BG values with clinical providers, creating a barrier to communication. Although many phone applications exist to help patients track BG values, most do not integrate with the electronic health record (EHR). Recent advances in technology can integrate the glucose meter (GM) data into the EHR. This pilot and feasibility study aimed to understand how an automated integration system of GM data into the EHR and remote monitoring by health care providers would impact patient-provider communication. Patients or parents of patients with T1D (n = 32, average hemoglobin A1c [HgbA1c]: 8.5%, SD: 1.7, average age: 13.9 years, SD: 3.8) who owned an Apple iPod^® or iPhone^® (5s or higher) participated, and their number of contacts through telephone calls or MyChart™ messages between clinic visits was recorded during each of the three phases: run-in, intervention, and learned. Twenty-eight families completed all phases, and despite guided review of BG trends and automated integration of BG values, the number of patient-initiated calls (P = 0.23) and HgbA1c values (P = 0.08) did not improve, nor was there a clinically significant change in the number of BG checks per day. Barriers to adoption and effectiveness of this technology exist, and patient motivation is still needed.

The effect of computerized decision support systems on cardiovascular risk factors: a systematic review and meta-analysis

Background

Cardiovascular risk management (CVRM) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease. Computerized decision support systems (CDSS) enable the clinician to integrate the latest scientific evidence and patient information into tailored strategies. The effect on cardiovascular risk factor management is yet to be confirmed.

Methods

We performed a systematic review and meta-analysis evaluating the effects of CDSS on CVRM, defined as the change in absolute values and attainment of treatment goals of systolic blood pressure (SBP), low density lipoprotein cholesterol (LDL-c) and HbA1c. Also, CDSS characteristics related to more effective CVRM were identified. Eligible articles were methodologically appraised using the Cochrane risk of bias tool. We calculated mean differences, relative risks, and if appropriate (I² < 70%), pooled the results using a random-effects model.

Results

Of the 14,335 studies identified, 22 were included. Four studies reported on SBP, 3 on LDL-c, 10 on CVRM in patients with type II diabetes and 5 on guideline adherence. The CDSSs varied considerably in technical performance and content. Heterogeneity of results was such that quantitative pooling was often not appropriate. Among CVRM patients, the results tended towards a beneficial effect of CDSS, but only LDL-c target attainment in diabetes patients reached statistical significance. Prompting, integration into the electronical health record, patient empowerment, and medication support were related to more effective CVRM.

Conclusion

We did not find a clear clinical benefit from CDSS in cardiovascular risk factor levels and target attainment. Some features of CDSS seem more promising than others. However, the variability in CDSS characteristics and heterogeneity of the results – emphasizing the immaturity of this research area - limit stronger conclusions. Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0824-x) contains supplementary material, which is available to authorized users.

Electronic clinical reminder and quality of primary diabetes care

AIMS

To study the association of EMR's clinical reminder use on a comprehensive set of diabetes quality metrics in U.S. office-based physicians and within solo- versus multi-physician practices. We conducted a retrospective cohort study on visits made by adults with diabetes identified from the National Ambulatory Medical Care Survey (2012-2014).

METHODS

Multiple logistic regression is used to test for associations between clinical reminder use and recommended services by the American Diabetes Association.

RESULTS

Of 5508 visits, nationally representing 112,978,791 visits, 31% received HbA1c tests, 13% received urinalysis test, and <10% received retinal or foot exams. Main effects of practice size and clinical reminder use were found for HbA1c, urinalysis, and foot exams. We find no statistically significant relationship to suggest that clinical reminder use improve diabetes process guidelines for solo practices.

CONCLUSIONS

Resource efforts, beyond clinical reminders, are needed to reduce gaps in primary diabetes care between solo and non-solo practices.

Effectiveness of an mHealth-Based Electronic Decision Support System for Integrated Management of Chronic Conditions in Primary Care: The mWellcare Cluster-Randomized Controlled Trial

BACKGROUND

The burden of noncommunicable diseases and their risk factors has rapidly increased worldwide, including in India. Innovative management strategies with electronic decision support and task sharing have been assessed for hypertension, diabetes mellitus, and depression individually, but an integrated package for multiple chronic condition management in primary care has not been evaluated.

METHODS

In a prospective, multicenter, open-label, cluster-randomized controlled trial involving 40 community health centers, using hypertension and diabetes mellitus as entry points, we evaluated the effectiveness of mWellcare, an mHealth system consisting of electronic health record storage and an electronic decision support for the integrated management of 5 chronic conditions (hypertension, diabetes mellitus, current tobacco and alcohol use, and depression) versus enhanced usual care among patients with hypertension and diabetes mellitus in India. At trial end (12-month follow-up), using intention-to-treat analysis, we examined the mean difference between arms in change in systolic blood pressure and glycated hemoglobin as primary outcomes and fasting blood glucose, total cholesterol, predicted 10-year risk of cardiovascular disease, depression score, and proportions reporting tobacco and alcohol use as secondary outcomes. Mixed-effects regression models were used to account for clustering and other confounding variables.

RESULTS

Among 3698 enrolled participants across 40 clusters (mean age, 55.1 years; SD, 11 years; 55.2% men), 3324 completed the trial. There was no evidence of difference between the 2 arms for systolic blood pressure (∆=-0.98; 95% CI, -4.64 to 2.67) and glycated hemoglobin (∆=0.11; 95% CI, -0.24 to 0.45) even after adjustment of several key variables (adjusted differences for systolic blood pressure: - 0.31 [95% CI, -3.91 to 3.29]; for glycated hemoglobin: 0.08 [95% CI, -0.27 to 0.44]). The mean withingroup changes in systolic blood pressure in mWellcare and enhanced usual care were -13.65 mmHg versus -12.66 mmHg, respectively, and for glycated hemoglobin were -0.48% and -0.58%, respectively. Similarly, there were no differences in the changes between the 2 groups for tobacco and alcohol use or other secondary outcomes.

CONCLUSIONS

We did not find an incremental benefit of mWellcare over enhanced usual care in the management of the chronic conditions studied.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT02480062.

Supporting general practitioner-based care for poorly controlled type 2 diabetes mellitus (the DECIDE study): feasibility study and protocol for a pilot cluster randomised controlled trial

BACKGROUND

Poorly controlled type 2 diabetes mellitus (T2DM) is associated with significant morbidity, mortality and healthcare costs. Control of T2DM can be challenging for healthcare professionals for a number of reasons, including poor concordance with medications, difficulties modifying lifestyle behaviour and also clinical inertia, which is defined as a reluctance among health professionals to intensify medications. A complex intervention, called ComputeriseD dECisIonal support for poorly controlleD typE 2 Diabetes mellitus in Irish General Practice (DECIDE), was developed, identifying T2DM patients with poor glycaemic and blood pressure control and aiming to target clinical inertia, by supporting therapeutic action, including GP-led medication intensification where appropriate. A small-scale, uncontrolled, non-randomised feasibility study highlighted the acceptability of the DECIDE intervention within Irish General Practice. This paper presents a protocol for a pilot cluster randomised controlled trial (RCT) of the DECIDE intervention.

METHODS/DESIGN

The pilot cluster RCT will involve 14 practices and 140 patients in Irish General Practice. Intervention GPs will participate in the DECIDE intervention, comprising (a) a training programme for the practices and (b) a web-based clinical decision support system supporting treatment escalation, tailored to specific patient information. Only patients who have poorly controlled T2DM (defined as HbA1c > 70 mmol/mol and/or BP > 150/95) will be included. The primary outcomes will include measures of feasibility such as recruitment and retention of practices and acceptability of the intervention and also HbA1c. Secondary outcomes will include medication intensification, blood pressure and lipids. Control GPs will continue to provide usual care. A process evaluation will be performed to determine whether the intervention is delivered as intended and treatment fidelity assessed to monitor and enhance the reliability and validity of interventions. An exploratory health economic analysis will examine the potential costs and cost effectiveness of the intervention relative to the control.

DISCUSSION

A pilot cluster RCT will establish the feasibility of a complex intervention which aims to support primary care for patients with poorly controlled T2DM in Irish General Practice.

TRIAL REGISTRATION

The protocol for the pilot cluster RCT is registered on the ISRCTN Registry at: ISRCTN69498919.

Comprehensive process model of clinical information interaction in primary care: results of a "best-fit" framework synthesis

Objective

To describe a new, comprehensive process model of clinical information interaction in primary care (Clinical Information Interaction Model, or CIIM) based on a systematic synthesis of published research.

Materials and Methods

We used the "best fit" framework synthesis approach. Searches were performed in PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Library and Information Science Abstracts, Library, Information Science and Technology Abstracts, and Engineering Village. Two authors reviewed articles according to inclusion and exclusion criteria. Data abstraction and content analysis of 443 published papers were used to create a model in which every element was supported by empirical research.

Results

The CIIM documents how primary care clinicians interact with information as they make point-of-care clinical decisions. The model highlights 3 major process components: (1) context, (2) activity (usual and contingent), and (3) influence. Usual activities include information processing, source-user interaction, information evaluation, selection of information, information use, clinical reasoning, and clinical decisions. Clinician characteristics, patient behaviors, and other professionals influence the process.

Discussion

The CIIM depicts the complete process of information interaction, enabling a grasp of relationships previously difficult to discern. The CIIM suggests potentially helpful functionality for clinical decision support systems (CDSSs) to support primary care, including a greater focus on information processing and use. The CIIM also documents the role of influence in clinical information interaction; influencers may affect the success of CDSS implementations.

Conclusion

The CIIM offers a new framework for achieving CDSS workflow integration and new directions for CDSS design that can support the work of diverse primary care clinicians.

Acceptance and barriers pertaining to a general practice decision support system for multiple clinical conditions: A mixed methods evaluation

Background

Many studies have investigated the use of clinical decision support systems as a means to improve care, but have thus far failed to show significant effects on patient-related outcomes. We developed a clinical decision support system that attempted to address issues that were identified in these studies. The system was implemented in Dutch general practice and was designed to be both unobtrusive and to respond in real time. Despite our efforts, usage of the system was low. In the current study we perform a mixed methods evaluation to identify remediable barriers which led to disappointing usage rates for our system.

Methods

A mixed methods evaluation employing an online questionnaire and focus group. The focus group was organized to clarify free text comments and receive more detailed feedback from general practitioners. Topics consisted of items based on results from the survey and additional open questions.

Results

The response rate for the questionnaire was 94%. Results from the questionnaire and focus group can be summarized as follows: The system was perceived as interruptive, despite its design. Participants felt that there were too many recommendations and that the relevance of the recommendations varied. Demographic based recommendations (e.g. age) were often irrelevant, while specific risk-based recommendations (e.g. diagnosis) were more relevant. The other main barrier to use was lack of time during the patient visit.

Conclusion

These results are likely to be useful to other researchers who are attempting to address the problems of interruption and alert fatigue in decision support.

Assessing the influence of health systems on Type 2 Diabetes Mellitus awareness, treatment, adherence, and control: A systematic review

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) is reported to affect one in 11 adults worldwide, with over 80% of T2DM patients residing in low-to-middle-income countries. Health systems play an integral role in responding to this increasing global prevalence, and are key to ensuring effective diabetes management. We conducted a systematic review to examine the health system-level factors influencing T2DM awareness, treatment, adherence, and control.

METHODS AND FINDINGS

A protocol for this study was published on the PROSPERO international prospective register of systematic reviews (PROSPERO 2016: CRD42016048185). Studies included in this review reported the effects of health systems factors, interventions, policies, or programmes on T2DM control, awareness, treatment, and adherence. The following databases were searched on 22 February 2017: Medline, Embase, Global health, LILACS, Africa-Wide, IMSEAR, IMEMR, and WPRIM. There were no restrictions on date, language, or study designs. Two reviewers independently screened studies for eligibility, extracted the data, and screened for risk of bias. Thereafter, we performed a narrative synthesis. A meta-analysis was not conducted due to methodological heterogeneity across different aspects of included studies. 93 studies were included for qualitative synthesis; 7 were conducted in LMICs. Through this review, we found two key health system barriers to effective T2DM care and management: financial constraints faced by the patient and limited access to health services and medication. We also found three health system factors that facilitate effective T2DM care and management: the use of innovative care models, increased pharmacist involvement in care delivery, and education programmes led by healthcare professionals.

CONCLUSIONS

This review points to the importance of reducing, or possibly eliminating, out-of-pocket costs for diabetes medication and self-monitoring supplies. It also points to the potential of adopting more innovative and integrated models of care, and the value of task-sharing of care with pharmacists. More studies which identify the effect of health system arrangements on various outcomes, particularly awareness, are needed.

Impact on process results of clinical decision support systems (CDSSs) applied to medication use: overview of systematic reviews

Objective

The purpose of this overview (systematic review of systematic reviews) is to evaluate the impact of clinical decision support systems (CDSS) applied to medication use in the care process.

Methods

A search for systematic reviews that address CDSS was performed on Medline following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Cochrane recommendations. Terms related to CDSS and systematic reviews were used in combination with Boolean operators and search field tags to build the electronic search strategy. There was no limitation of date or language for inclusion. We included revisions that investigated, as a main or secondary objective, changes in process outcomes. The Revised Assessment of Multiple Systematic Reviews (R-AMSTAR) score was used to evaluate the quality of the studies.

Results

The search retrieved 954 articles. Five articles were added through manual search, totaling an initial sample of 959 articles. After screening and reading in full, 44 systematic reviews met the inclusion criteria. In the medication-use processes where CDSS was used, the most common stages were prescribing (n=38 (86.36%) and administering (n=12 (27.27%)). Most of the systematic reviews demonstrated improvement in the health care process (30/44 - 68.2%). The main positive results were related to improvement of the quality of prescription by the physicians (14/30 - 46.6%) and reduction of errors in prescribing (5/30 - 16.6%). However, the quality of the studies was poor, according to the score used.

Conclusion

CDSSs represent a promising technology to optimize the medication-use process, especially related to improvement in the quality of prescriptions and reduction of prescribing errors, although higher quality studies are needed to establish the predictors of success in these systems.

Development of a complex intervention to promote appropriate prescribing and medication intensification in poorly controlled type 2 diabetes mellitus in Irish general practice

BACKGROUND

Poorly controlled type 2 diabetes mellitus (T2DM) can be seen as failure to meet recommended targets for management of key risk factors including glycaemic control, blood pressure and lipids. Poor control of risk factors is associated with significant morbidity, mortality and healthcare costs. Failure to intensify medications for patients with poor control of T2DM when indicated is called clinical inertia and is one contributory factor to poor control of T2DM. We aimed to develop a theory and evidence-based complex intervention to improve appropriate prescribing and medication intensification in poorly controlled T2DM in Irish general practice.

METHODS

The first stage of the Medical Research Council Framework for developing and evaluating complex interventions was utilised. To identify current evidence, we performed a systematic review to examine the effectiveness of interventions targeting patients with poorly controlled T2DM in community settings. The Behaviour Change Wheel theoretical approach was used to identify suitable intervention functions. Workshops, simulation, collaborations with academic partners and observation of physicians were utilised to operationalise the intervention functions and design the elements of the complex intervention.

RESULTS

Our systematic review highlighted that professional-based interventions, potentially through clinical decision support systems, could address poorly controlled T2DM. Appropriate intensification of anti-glycaemic and cardiovascular medications, by general practitioners (GPs), for adults with poorly controlled T2DM was identified as the key behaviour to address clinical inertia. Psychological capability was the key driver of the behaviour, which needed to change, suggesting five key intervention functions (education, training, enablement, environmental restructuring and incentivisation) and nine key behaviour change techniques, which were operationalised into a complex intervention. The intervention has three components: (a) a training program/academic detailing of target GPs, (b) a remote finder tool to help GPs identify patients with poor control of T2DM in their practice and (c) A web-based clinical decision support system.

CONCLUSIONS

This paper describes a multifaceted process including an exploration of current evidence and a thorough theoretical understanding of the predictors of the behaviour resulting in the design of a complex intervention to promote the implementation of evidence-based guidelines, through appropriate prescribing and medication intensification in poorly controlled T2DM.

Improving risk factor management for patients with poorly controlled type 2 diabetes: a systematic review of healthcare interventions in primary care and community settings

OBJECTIVES

Poorly controlled type 2 diabetes mellitus (T2DM) is a major international health problem. Our aim was to assess the effectiveness of healthcare interventions, specifically targeting patients with poorly controlled T2DM, which seek to improve glycaemic control and cardiovascular risk in primary care settings.

DESIGN

Systematic review.

SETTING

Primary care and community settings.

INCLUDED STUDIES

Randomised controlled trials (RCTs) targeting patients with poor glycaemic control were identified from Pubmed, Embase, Web of Science, Cochrane Library and SCOPUS. Poor glycaemic control was defined as HbA1c over 59 mmol/mol (7.5%).

INTERVENTIONS

Interventions were classified as organisational, patient-oriented, professional, financial or regulatory.

OUTCOMES

Primary outcomes were HbA1c, blood pressure and lipid control. Two reviewers independently assessed studies for eligibility, extracted data and assessed study quality. Meta-analyses were undertaken where appropriate using random-effects models. Subgroup analysis explored the effects of intervention type, baseline HbA1c, study quality and study duration. Meta-regression analyses were undertaken to investigate identified heterogeneity.

RESULTS

Forty-two RCTs were identified, including 11 250 patients, with most undertaken in USA. In general, studies had low risk of bias. The main intervention types were patient-directed (48%) and organisational (48%). Overall, interventions reduced HbA1c by -0.34% (95% CI -0.46% to -0.22%), but meta-analyses had high statistical heterogeneity. Subgroup analyses suggested that organisational interventions and interventions on those with baseline HbA1c over 9.5% had better improvements in HbA1c. Meta-regression analyses suggested that only interventions on those with population HbA1c over 9.5% were more effective. Interventions had a modest improvement of blood pressure and lipids, although baseline levels of control were generally good.

CONCLUSIONS

This review suggests that interventions for T2DM, in primary care, are better targeted at individuals with very poor glycaemic control and that organisational interventions may be more effective.

Development of a clinical decision support system for diabetes care: A pilot study

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

ACC/AHA Special Report: Clinical Practice Guideline Implementation Strategies: A Summary of Systematic Reviews by the NHLBI Implementation Science Work Group: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines

BACKGROUND

In 2008, the National Heart, Lung, and Blood Institute convened an Implementation Science Work Group to assess evidence-based strategies for effectively implementing clinical practice guidelines. This was part of a larger effort to update existing clinical practice guidelines on cholesterol, blood pressure, and overweight/obesity.

OBJECTIVES

Review evidence from the published implementation science literature and identify effective or promising strategies to enhance the adoption and implementation of clinical practice guidelines.

METHODS

This systematic review was conducted on 4 critical questions, each focusing on the adoption and effectiveness of 4 intervention strategies: (1) reminders, (2) educational outreach visits, (3) audit and feedback, and (4) provider incentives. A scoping review of the Rx for Change database of systematic reviews was used to identify promising guideline implementation interventions aimed at providers. Inclusion and exclusion criteria were developed a priori for each question, and the published literature was initially searched up to 2012, and then updated with a supplemental search to 2015. Two independent reviewers screened the returned citations to identify relevant reviews and rated the quality of each included review.

RESULTS

Audit and feedback and educational outreach visits were generally effective in improving both process of care (15 of 21 reviews and 12 of 13 reviews, respectively) and clinical outcomes (7 of 12 reviews and 3 of 5 reviews, respectively). Provider incentives showed mixed effectiveness for improving both process of care (3 of 4 reviews) and clinical outcomes (3 reviews equally distributed between generally effective, mixed, and generally ineffective). Reminders showed mixed effectiveness for improving process of care outcomes (27 reviews with 11 mixed and 3 generally ineffective results) and were generally ineffective for clinical outcomes (18 reviews with 6 mixed and 9 generally ineffective results). Educational outreach visits (2 of 2 reviews), reminders (3 of 4 reviews), and provider incentives (1 of 1 review) were generally effective for cost reduction. Educational outreach visits (1 of 1 review) and provider incentives (1 of 1 review) were also generally effective for cost-effectiveness outcomes. Barriers to clinician adoption or adherence to guidelines included time constraints (8 reviews/overviews); limited staffing resources (2 overviews); timing (5 reviews/overviews); clinician skepticism (5 reviews/overviews); clinician knowledge of guidelines (4 reviews/overviews); and higher age of the clinician (1 overview). Facilitating factors included guideline characteristics such as format, resources, and end-user involvement (6 reviews/overviews); involving stakeholders (5 reviews/overviews); leadership support (5 reviews/overviews); scope of implementation (5 reviews/overviews); organizational culture such as multidisciplinary teams and low-baseline adherence (9 reviews/overviews); and electronic guidelines systems (3 reviews).

CONCLUSION

The strategies of audit and feedback and educational outreach visits were generally effective in improving both process of care and clinical outcomes. Reminders and provider incentives showed mixed effectiveness, or were generally ineffective. No general conclusion could be reached about cost effectiveness, because of limitations in the evidence. Important gaps exist in the evidence on effectiveness of implementation interventions, especially regarding clinical outcomes, cost effectiveness and contextual issues affecting successful implementation.

The Application of Genomics in Diabetes: Barriers to Discovery and Implementation

The emerging availability of genomic and electronic health data in large populations is a powerful tool for research that has drawn interest in bringing precision medicine to diabetes. In this article, we discuss the potential application of genomics to the prediction, prevention, and treatment of diabetes, and we use examples from other areas of medicine to illustrate some of the challenges involved in conducting genomics research in human populations and implementing findings in practice. At this time, a major barrier to the application of genomics in diabetes care is the lack of actionable genomic findings. Whether genomic information should be used in clinical practice requires a framework for evaluating the validity and clinical utility of this approach, an improved integration of genomic data into electronic health records, and the clinical decision support and educational resources for clinicians to use these data. Efforts to identify optimal approaches in all of these domains are in progress and may help to bring diabetes into the era of genomic medicine.

Do false positive alerts in naïve clinical decision support system lead to false adoption by physicians? A randomized controlled trial

OBJECTIVES

False positive alerts in patient-safety-related clinical decision support systems (CDSS) are defined as alerts which incorrectly prompt when no-risk patients are encountered. It is an unfavorable condition which may potentially mislead physicians. The aim is to investigate physician responses toward false positive (FP) and true positive (TP) alerts in CDSS for the prevention of contrast-induced nephropathy (CIN).

METHODS

A two-arm cluster randomized controlled trial was conducted in university hospitals. Eligible physicians were randomized to receive alert intervention or no intervention (groups 1 and 2, respectively). The alert system was embedded with a deliberately non-specific risk detection tool in order to generate TP and FP alerts. The naïve alert system would alert the physician to cancel the order regardless of the patient being at-risk or not at-risk. CIN risk was stratified as at-risk and no-risk according to a patient's pre-existing renal function. Contrast imaging order-cancellation rate was measured as primary outcome.

RESULTS

3802 contrast-enhanced examination orders from 66 physicians were analyzed. Demographic data and risk distributions of patients were similar and well-balanced between two groups. In the intervention group, a total of 1892 alerts were generated (332 TP alerts and 1560 FP alerts). Order-cancellation rates were 5.1% versus 1.4% in groups 1 and 2 for at-risk patients (relative risk [RR] = 3.69) from TP alerts, and 1.0% versus 1.4% for no-risk patients (RR = 0.71) from FP alerts. Using generalized linear model with generalized estimating equation, the FP alerts had no order-cancellation effect when compared to the control arm (adjusted RR = 0.69; 95%CI, 0.36-1.32). The TP alerts had a larger order-cancellation effect than that of the control arm (adjusted RR = 2.95; 95%CI, 0.94-9.27), which revealed a marginal trend toward significance. However, the effect was not statistically significant (adjusted RR = 1.24; 95%CI, 0.71-2.18) if TP and FP alerts were mixed.

CONCLUSIONS

Physicians are not likely to adopt recommendations provided by false positive alerts in patient-safety-related CDSS. If reporting only the adoption rate of CDSS as a whole without differentiating between TP and FP alerts, the effects of TP and FP alerts will be mixed, and thus, will lead to an underestimation of system effectiveness.

The eClinical Care Pathway Framework: a novel structure for creation of online complex clinical care pathways and its application in the management of sexually transmitted infections

BACKGROUND

Despite considerable international eHealth impetus, there is no guidance on the development of online clinical care pathways. Advances in diagnostics now enable self-testing with home diagnosis, to which comprehensive online clinical care could be linked, facilitating completely self-directed, remote care. We describe a new framework for developing complex online clinical care pathways and its application to clinical management of people with genital chlamydia infection, the commonest sexually transmitted infection (STI) in England.

METHODS

Using the existing evidence-base, guidelines and examples from contemporary clinical practice, we developed the eClinical Care Pathway Framework, a nine-step iterative process. Step 1: define the aims of the online pathway; Step 2: define the functional units; Step 3: draft the clinical consultation; Step 4: expert review; Step 5: cognitive testing; Step 6: user-centred interface testing; Step 7: specification development; Step 8: software testing, usability testing and further comprehension testing; Step 9: piloting. We then applied the Framework to create a chlamydia online clinical care pathway (Online Chlamydia Pathway).

RESULTS

Use of the Framework elucidated content and structure of the care pathway and identified the need for significant changes in sequences of care (Traditional: history, diagnosis, information versus Online: diagnosis, information, history) and prescribing safety assessment. The Framework met the needs of complex STI management and enabled development of a multi-faceted, fully-automated consultation.

CONCLUSION

The Framework provides a comprehensive structure on which complex online care pathways such as those needed for STI management, which involve clinical services, public health surveillance functions and third party (sexual partner) management, can be developed to meet national clinical and public health standards. The Online Chlamydia Pathway's standardised method of collecting data on demographics and sexual behaviour, with potential for interoperability with surveillance systems, could be a powerful tool for public health and clinical management.

Outpatient diabetes clinical decision support: current status and future directions

Outpatient clinical decision support systems have had an inconsistent impact on key aspects of diabetes care. A principal barrier to success has been low use rates in many settings. Here, we identify key aspects of clinical decision support system design, content and implementation that are related to sustained high use rates and positive impacts on glucose, blood pressure and lipid management. Current diabetes clinical decision support systems may be improved by prioritizing care recommendations, improving communication of treatment-relevant information to patients, using such systems for care coordination and case management and integrating patient-reported information and data from remote devices into clinical decision algorithms and interfaces.

Development and Implementation of a Collaborative Team Care Model for Effective Insulin Use in an Academic Medical Center Primary Care Network

Improving glycemic control across a primary care diabetes population is challenging. This article describes the development, implementation, and outcomes of the Diabetes Care Collaborative Model (DCCM), a collaborative team care process focused on promoting effective insulin use targeting patients with hyperglycemia in a patient-centered medical home model. After a pilot, the DCCM was implemented in 18 primary care practices affiliated with an academic medical center. Its implementation was associated with improvements in glycemic control and increase in insulin prescription longitudinally and across the entire population, with a >1% reduction in the proportion of glycated hemoglobin >9% at 2 years after the implementation compared with the 2 years prior ( P < .001). Facilitating factors included diverse stakeholder engagement, institutional alignment of priorities, awarding various types of credits for participation and implementation to providers, and a strong theoretical foundation using the principles of the collaborative care model.

Automated integration of continuous glucose monitor data in the electronic health record using consumer technology

The diabetes healthcare provider plays a key role in interpreting blood glucose trends, but few institutions have successfully integrated patient home glucose data in the electronic health record (EHR). Published implementations to date have required custom interfaces, which limit wide-scale replication. We piloted automated integration of continuous glucose monitor data in the EHR using widely available consumer technology for 10 pediatric patients with insulin-dependent diabetes. Establishment of a passive data communication bridge via a patient’s/parent’s smartphone enabled automated integration and analytics of patient device data within the EHR between scheduled clinic visits. It is feasible to utilize available consumer technology to assess and triage home diabetes device data within the EHR, and to engage patients/parents and improve healthcare provider workflow.

The Influence of Clinical Decision Support on Diagnostic Accuracy in Nurse Practitioners

BACKGROUND

Clinical decision support systems (CDSSs) at the point of care are evidence-based interventions that have demonstrated incremental positive impact on quality of healthcare delivery over the past two decades. Existing best practices inform strategies to promote adoption and achievement of targeted outcomes. The purpose of this improvement project was to conduct a pilot implementation to understand the contextual factors and readiness for dissemination of a newly acquired electronic CDSS by evaluating its influence on diagnostic accuracy in nurse practitioners (NPs) functioning in a community health setting.

AIMS

The specific aims of this project were to measure and compare diagnostic accuracy in a pilot group before and after CDSS use, educate clinicians about the system and pilot its use, and then leverage the experience to design the practice-wide CDSS dissemination strategy.

METHODS

The project engaged a subset of NPs from a home-based primary care practice and other organizational stakeholders who provided tangible support and necessary resources for successful adoption of this innovation in practice. A structured conceptual model of Evidence-Based Practice Improvement enhanced with elements of the Promoting Action on Research Implementation in Health Sciences framework was used to guide the development, implementation and evaluation of this practice improvement initiative. A group of seven NP representatives of the practice participated in the project. Data collection was composed of small tests of change (plan-do-study-act) cycles at the local practice level, measuring achievement of improvement of the chosen outcome of correctness of medical diagnosis evidenced by appropriate substantiating clinical documentation.

LINKING EVIDENCE TO ACTION

In this home visiting primary care practice setting, use of a CDSS by NPs achieved eventual improvement in diagnostic accuracy and clinical documentation, as demonstrated on chart audit, though the improvement was not sustained over time. The pilot findings ultimately informed decision making about CDSS dissemination strategies and future uses of the system. When appropriately deployed, a CDSS offers the potential to improve professional practice, support differential diagnosis and evidence-based treatment, and provide detailed information about the disease state to facilitate robust clinical documentation.

Clinical decision support improves physician guideline adherence for laboratory monitoring of chronic kidney disease: a matched cohort study

Background

Guidelines exist for chronic kidney disease (CKD) but are not well implemented in clinical practice. We evaluated the impact of a guideline-based clinical decision support system (CDSS) on laboratory monitoring and achievement of laboratory targets in stage 3–4 CKD patients.

Methods

We performed a matched cohort study of 12,353 stage 3–4 CKD patients whose physicians opted to receive an automated guideline-based CDSS with CKD-related lab results, and 42,996 matched controls whose physicians did not receive the CDSS. Physicians were from US community-based physician practices utilizing a large, commercial laboratory (LabCorp®).

We compared the percentage of laboratory tests obtained within guideline-recommended intervals and the percentage of results within guideline target ranges between CDSS and non-CDSS patients. Laboratory tests analyzed included estimated glomerular filtration rate, plasma parathyroid hormone, serum calcium, phosphorus, 25-hydroxy vitamin D (25-D), total carbon dioxide, transferrin saturation (TSAT), LDL cholesterol (LDL-C), blood hemoglobin, and urine protein measurements.

Results

Physicians who used the CDSS ordered all CKD-relevant testing more in accord with guidelines than those who did not use the system. Odds ratios favoring CDSS ranged from 1.29 (TSAT) to 1.88 (serum phosphorus) [CI, 1.20 to 2.01], p < 0.001 for all tests. The CDSS impact was greater for primary care physicians versus nephrologists. CDSS physicians met guideline targets for LDL-C and 25-D more often, but hemoglobin targets less often, than non-CDSS physicians. Use of CDSS did not impact guideline target achievement for the remaining tests.

Conclusions

Use of an automated laboratory-based CDSS may improve physician adherence to guidelines with respect to timely monitoring of CKD.

Electronic supplementary material

The online version of this article (doi:10.1186/s12882-015-0159-5) contains supplementary material, which is available to authorized users.

Integrating computerized clinical decision support systems into clinical work: A meta-synthesis of qualitative research

PURPOSE

Computerized clinical decision support systems (CDSS) are an emerging means for improving healthcare safety, quality and efficiency, but meta-analyses findings are mixed. This meta-synthesis aggregates qualitative research findings as possible explanations for variable quantitative research outcomes.

INCLUSION CRITERIA

Qualitative studies published between 2000 and 2013 in English, involving physicians, registered and advanced practice nurses' experience of CDSS use in clinical practice were included.

SEARCH STRATEGY

PubMed and CINAHL databases were searched. Study titles and abstracts were screened against inclusion criteria. Retained studies were appraised against quality criteria. Findings were extracted iteratively from studies in the 4th quartile of quality scores. Two reviewers constructed themes inductively. A third reviewer applied the defined themes deductively achieving 92% agreement.

RESULTS

3798 unique records were returned; 56 met inclusion criteria and were reviewed against quality criteria. 9 studies were of sufficiently high quality for synthetic analysis. Five major themes (clinician-patient-system integration; user interface usability; the need for better 'algorithms'; system maturity; patient safety) were defined.

CONCLUSIONS

Despite ongoing development, CDSS remains an emerging technology. Lack of understanding about and lack of consideration for the interaction between human decision makers and CDSS is a major reason for poor system adoption and use. Further high-quality qualitative research is needed to better understand human-system interaction issues. These issues may continue to confound quantitative study results if not addressed.

Effectiveness of computerized clinical decision support systems for asthma and chronic obstructive pulmonary disease in primary care: a systematic review

BACKGROUND

The use of computerized clinical decision support systems may improve the diagnosis and ongoing management of chronic diseases, which requires recurrent visits to multiple health professionals, disease and medication monitoring and modification of patient behavior. The aim of this review was to systematically review randomized controlled trials evaluating the effectiveness of computerized clinical decision systems (CCDSS) in the care of people with asthma and COPD.

METHODS

Randomized controlled trials published between 2003 and 2013 were searched using multiple electronic databases Medline, EMBASE, CINAHL, IPA, Informit, PsychINFO, Compendex, and Cochrane Clinical Controlled Trials Register databases. To be included, RCTs had to evaluate the role of the CCDSSs for asthma and/or COPD in primary care.

RESULTS

Nineteen studies representing 16 RCTs met our inclusion criteria. The majority of the trials were conducted in patients with asthma. Study quality was generally high. Meta-analysis was not conducted because of methodological and clinical heterogeneity. The use of CCDSS improved asthma and COPD care in 14 of the 19 studies reviewed (74%). Nine of the nineteen studies showed statistically significant (p < 0.05) improvement in the primary outcomes measured. The majority of the studies evaluated health care process measures as their primary outcomes (10/19).

CONCLUSION

Evidence supports the effectiveness of CCDSS in the care of people with asthma. However there is very little information of its use in COPD care. Although there is considerable improvement in the health care process measures and clinical outcomes through the use of CCDSSs, its effects on user workload and efficiency, safety, costs of care, provider and patient satisfaction remain understudied.