Cost-effectiveness of the diabetes care protocol, a multifaceted computerized decision support diabetes management intervention that reduces cardiovascular risk

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

BACKGROUND

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.

OBJECTIVE

This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.

METHODS

We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.

RESULTS

Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.

CONCLUSIONS

CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.37766/inplasy2022.9.0061.

The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment

In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented.

The effects of computerised decision support systems on nursing and allied health professional performance and patient outcomes: a systematic review and user contextualisation

Background

Computerised decision support systems (CDSS) are widely used by nurses and allied health professionals but their effect on clinical performance and patient outcomes is uncertain.

Objectives

Evaluate the effects of clinical decision support systems use on nurses', midwives' and allied health professionals' performance and patient outcomes and sense-check the results with developers and users.

Eligibility criteria

Comparative studies (randomised controlled trials (RCTs), non-randomised trials, controlled before-and-after (CBA) studies, interrupted time series (ITS) and repeated measures studies comparing) of CDSS versus usual care from nurses, midwives or other allied health professionals.

Information sources

Nineteen bibliographic databases searched October 2019 and February 2021.

Risk of bias

Assessed using structured risk of bias guidelines; almost all included studies were at high risk of bias.

Synthesis of results

Heterogeneity between interventions and outcomes necessitated narrative synthesis and grouping by: similarity in focus or CDSS-type, targeted health professionals, patient group, outcomes reported and study design.

Included studies

Of 36,106 initial records, 262 studies were assessed for eligibility, with 35 included: 28 RCTs (80%), 3 CBA studies (8.6%), 3 ITS (8.6%) and 1 non-randomised trial, a total of 1318 health professionals and 67,595 patient participants. Few studies were multi-site and most focused on decision-making by nurses (71%) or paramedics (5.7%). Standalone, computer-based CDSS featured in 88.7% of the studies; only 8.6% of the studies involved 'smart' mobile or handheld technology. Care processes - including adherence to guidance - were positively influenced in 47% of the measures adopted. For example, nurses' adherence to hand disinfection guidance, insulin dosing, on-time blood sampling, and documenting care were improved if they used CDSS. Patient care outcomes were statistically - if not always clinically - significantly improved in 40.7% of indicators. For example, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity, and accurate triaging were features of professionals using CDSS compared to those who were not.

Evidence limitations

Allied health professionals (AHPs) were underrepresented compared to nurses; systems, studies and outcomes were heterogeneous, preventing statistical aggregation; very wide confidence intervals around effects meant clinical significance was questionable; decision and implementation theory that would have helped interpret effects - including null effects - was largely absent; economic data were scant and diverse, preventing estimation of overall cost-effectiveness.

Interpretation

CDSS can positively influence selected aspects of nurses', midwives' and AHPs' performance and care outcomes. Comparative research is generally of low quality and outcomes wide ranging and heterogeneous. After more than a decade of synthesised research into CDSS in healthcare professions other than medicine, the effect on processes and outcomes remains uncertain. Higher-quality, theoretically informed, evaluative research that addresses the economics of CDSS development and implementation is still required.

Future work

Developing nursing CDSS and primary research evaluation.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in Health and Social Care Delivery Research; 2023. See the NIHR Journals Library website for further project information.

Registration

PROSPERO [number: CRD42019147773].

Effects of computerised clinical decision support systems (CDSS) on nursing and allied health professional performance and patient outcomes: a systematic review of experimental and observational studies

OBJECTIVE

Computerised clinical decision support systems (CDSS) are an increasingly important part of nurse and allied health professional (AHP) roles in delivering healthcare. The impact of these technologies on these health professionals' performance and patient outcomes has not been systematically reviewed. We aimed to conduct a systematic review to investigate this.

MATERIALS AND METHODS

The following bibliographic databases and grey literature sources were searched by an experienced Information Professional for published and unpublished research from inception to February 2021 without language restrictions: MEDLINE (Ovid), Embase Classic+Embase (Ovid), PsycINFO (Ovid), HMIC (Ovid), AMED (Allied and Complementary Medicine) (Ovid), CINAHL (EBSCO), Cochrane Central Register of Controlled Trials (Wiley), Cochrane Database of Systematic Reviews (Wiley), Social Sciences Citation Index Expanded (Clarivate), ProQuest Dissertations & Theses Abstracts & Index, ProQuest ASSIA (Applied Social Science Index and Abstract), Clinical Trials.gov, WHO International Clinical Trials Registry (ICTRP), Health Services Research Projects in Progress (HSRProj), OpenClinical(www.OpenClinical.org), OpenGrey (www.opengrey.eu), Health.IT.gov, Agency for Healthcare Research and Quality (www.ahrq.gov). Any comparative research studies comparing CDSS with usual care were eligible for inclusion.

RESULTS

A total of 36 106 non-duplicate records were identified. Of 35 included studies: 28 were randomised trials, three controlled-before-and-after studies, three interrupted-time-series and one non-randomised trial. There were ~1318 health professionals and ~67 595 patient participants in the studies. Most studies focused on nurse decision-makers (71%) or paramedics (5.7%). CDSS as a standalone Personal Computer/LAPTOP-technology was a feature of 88.7% of the studies; only 8.6% of the studies involved 'smart' mobile/handheld-technology.

DISCUSSION

CDSS impacted 38% of the outcome measures used positively. Care processes were better in 47% of the measures adopted; examples included, nurses' adherence to hand disinfection guidance, insulin dosing, on-time blood sampling and documenting care. Patient care outcomes in 40.7% of indicators were better; examples included, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity and triaging appropriateness.

CONCLUSION

CDSS may have a positive impact on selected aspects of nurses' and AHPs' performance and care outcomes. However, comparative research is generally low quality, with a wide range of heterogeneous outcomes. After more than 13 years of synthesised research into CDSS in healthcare professions other than medicine, the need for better quality evaluative research remains as pressing.

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations

There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.

Cost-effectiveness of Interventions to Manage Diabetes: Has the Evidence Changed Since 2008?

OBJECTIVE

To synthesize updated evidence on the cost-effectiveness (CE) of interventions to manage diabetes, its complications, and comorbidities.

RESEARCH DESIGN AND METHODS

We conducted a systematic literature review of studies from high-income countries evaluating the CE of diabetes management interventions recommended by the American Diabetes Association (ADA) and published in English between June 2008 and July 2017. We also incorporated studies from a previous CE review from the period 1985-2008. We classified the interventions based on their strength of evidence (strong, supportive, or uncertain) and levels of CE: cost-saving (more health benefit at a lower cost), very cost-effective (≤$25,000 per life year gained [LYG] or quality-adjusted life year [QALY]), cost-effective ($25,001-$50,000 per LYG or QALY), marginally cost-effective ($50,001-$100,000 per LYG or QALY), or not cost-effective (>$100,000 per LYG or QALY). Costs were measured in 2017 U.S. dollars.

RESULTS

Seventy-three new studies met our inclusion criteria. These were combined with 49 studies from the previous review to yield 122 studies over the period 1985-2017. A large majority of the ADA-recommended interventions remain cost-effective. Specifically, we found strong evidence that the following ADA-recommended interventions are cost-saving or very cost-effective: In the cost-saving category are 1) ACE inhibitor (ACEI)/angiotensin receptor blocker (ARB) therapy for intensive hypertension management compared with standard hypertension management, 2) ACEI/ARB therapy to prevent chronic kidney disease and/or end-stage renal disease in people with albuminuria compared with no ACEI/ARB therapy, 3) comprehensive foot care and patient education to prevent and treat foot ulcers among those at moderate/high risk of developing foot ulcers, 4) telemedicine for diabetic retinopathy screening compared with office screening, and 5) bariatric surgery compared with no surgery for individuals with type 2 diabetes (T2D) and obesity (BMI ≥30 kg/m²). In the very cost-effective category are 1) intensive glycemic management (targeting A1C <7%) compared with conventional glycemic management (targeting an A1C level of 8-10%) for individuals with newly diagnosed T2D, 2) multicomponent interventions (involving behavior change/education and pharmacological therapy targeting hyperglycemia, hypertension, dyslipidemia, microalbuminuria, nephropathy/retinopathy, secondary prevention of cardiovascular disease with aspirin) compared with usual care, 3) statin therapy compared with no statin therapy for individuals with T2D and history of cardiovascular disease, 4) diabetes self-management education and support compared with usual care, 5) T2D screening every 3 years starting at age 45 years compared with no screening, 6) integrated, patient-centered care compared with usual care, 7) smoking cessation compared with no smoking cessation, 8) daily aspirin use as primary prevention for cardiovascular complications compared with usual care, 9) self-monitoring of blood glucose three times per day compared with once per day among those using insulin, 10) intensive glycemic management compared with conventional insulin therapy for T2D among adults aged ≥50 years, and 11) collaborative care for depression compared with usual care.

CONCLUSIONS

Complementing professional treatment recommendations, our systematic review provides an updated understanding of the potential value of interventions to manage diabetes and its complications and can assist clinicians and payers in prioritizing interventions and health care resources.

Cost-effectiveness of a primary care multidisciplinary Risk Assessment and Management Program for patients with diabetes mellitus (RAMP-DM) over lifetime

PURPOSE

The multidisciplinary Risk Assessment and Management Program for patients with diabetes mellitus (RAMP-DM) was found to be cost-saving in comparison with usual primary care over 5 years' follow-up. This study aimed to estimate the cost-effectiveness of RAMP-DM over lifetime.

METHODS

We built a Discrete Event Simulation model to evaluate the cost-effectiveness of RAMP-DM over lifespan from public health service provider's perspective. Transition probabilities among disease states were extrapolated from a cohort of 17,140 propensity score matched participants in RAMP-DM and those under usual primary care over 5-year's follow-up. The mortality of patients with specific DM-related complications was estimated from a cohort of 206,238 patients with diabetes. Health preference and direct medical costs of DM patients referred to our previous studies among Chinese DM patients.

RESULTS

RAMP-DM individuals gained 0.745 QALYs and cost US$1404 less than those under usual care. The probabilistic sensitivity analysis found that RAMP-DM had 86.0% chance of being cost-saving compared to usual care under the assumptions and estimates used in the model. The probability of RAMP-DM being cost-effective compared to usual care would be over 99%, when the willingness to pay threshold is HK$20,000 (US$ 2564) or higher.

CONCLUSION

RAMP-DM added to usual primary care was cost-saving in managing people with diabetes over lifetime. These findings support the integration of RAMP-DM as part of routine primary care for all patients with diabetes.

Economic evaluations of eHealth technologies: A systematic review

BACKGROUND

Innovations in eHealth technologies have the potential to help older adults live independently, maintain their quality of life, and to reduce their health system dependency and health care expenditure. The objective of this study was to systematically review and appraise the quality of cost-effectiveness or utility studies assessing eHealth technologies in study populations involving older adults.

METHODS

We systematically searched multiple databases (MEDLINE, EMBASE, CINAHL, NHS EED, and PsycINFO) for peer-reviewed studies published in English from 2000 to 2016 that examined cost-effectiveness (or utility) of eHealth technologies. The reporting quality of included studies was appraised using the Consolidated Health Economic Evaluation Reporting Standards statement.

RESULTS

Eleven full text articles met the inclusion criteria representing public and private health care systems. eHealth technologies evaluated by these studies includes computerized decision support system, a web-based physical activity intervention, internet-delivered cognitive behavioral therapy, telecare, and telehealth. Overall, the reporting quality of the studies included in the review was varied. Most studies demonstrated efficacy and cost-effectiveness of an intervention using a randomized control trial and statistical modeling, respectively. This review found limited information on the feasibility of adopting these technologies based on economic and organizational factors.

CONCLUSIONS

This review identified few economic evaluations of eHealth technologies that included older adults. The quality of the current evidence is limited and further research is warranted to clearly demonstrate the long-term cost-effectiveness of eHealth technologies from the health care system and societal perspectives.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Background

Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.

Objective

The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.

Methods

A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.

Results

We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.

Conclusions

We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review

Objective

This review evaluates costs and benefits associated with acquiring, implementing, and operating clinical decision support systems (CDSSs) to prevent cardiovascular disease (CVD).

Materials and Methods

Methods developed for the Community Guide were used to review CDSS literature covering the period from January 1976 to October 2015. Twenty-one studies were identified for inclusion.

Results

It was difficult to draw a meaningful estimate for the cost of acquiring and operating CDSSs to prevent CVD from the available studies ( n  = 12) due to considerable heterogeneity. Several studies ( n  = 11) indicated that health care costs were averted by using CDSSs but many were partial assessments that did not consider all components of health care. Four cost-benefit studies reached conflicting conclusions about the net benefit of CDSSs based on incomplete assessments of costs and benefits. Three cost-utility studies indicated inconsistent conclusions regarding cost-effectiveness based on a conservative $50,000 threshold.

Discussion

Intervention costs were not negligible, but specific estimates were not derived because of the heterogeneity of implementation and reporting metrics. Expected economic benefits from averted health care cost could not be determined with confidence because many studies did not fully account for all components of health care.

Conclusion

We were unable to conclude whether CDSSs for CVD prevention is either cost-beneficial or cost-effective. Several evidence gaps are identified, most prominently a lack of information about major drivers of cost and benefit, a lack of standard metrics for the cost of CDSSs, and not allowing for useful life of a CDSS that generally extends beyond one accounting period.

Risk scoring for the primary prevention of cardiovascular disease

BACKGROUND

The current paradigm for cardiovascular disease (CVD) emphasises absolute risk assessment to guide treatment decisions in primary prevention. Although the derivation and validation of multivariable risk assessment tools, or CVD risk scores, have attracted considerable attention, their effect on clinical outcomes is uncertain.

OBJECTIVES

To assess the effects of evaluating and providing CVD risk scores in adults without prevalent CVD on cardiovascular outcomes, risk factor levels, preventive medication prescribing, and health behaviours.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library (2016, Issue 2), MEDLINE Ovid (1946 to March week 1 2016), Embase (embase.com) (1974 to 15 March 2016), and Conference Proceedings Citation Index-Science (CPCI-S) (1990 to 15 March 2016). We imposed no language restrictions. We searched clinical trial registers in March 2016 and handsearched reference lists of primary studies to identify additional reports.

SELECTION CRITERIA

We included randomised and quasi-randomised trials comparing the systematic provision of CVD risk scores by a clinician, healthcare professional, or healthcare system compared with usual care (i.e. no systematic provision of CVD risk scores) in adults without CVD.

DATA COLLECTION AND ANALYSIS

Three review authors independently selected studies, extracted data, and evaluated study quality. We used the Cochrane 'Risk of bias' tool to assess study limitations. The primary outcomes were: CVD events, change in CVD risk factor levels (total cholesterol, systolic blood pressure, and multivariable CVD risk), and adverse events. Secondary outcomes included: lipid-lowering and antihypertensive medication prescribing in higher-risk people. We calculated risk ratios (RR) for dichotomous data and mean differences (MD) or standardised mean differences (SMD) for continuous data using 95% confidence intervals. We used a fixed-effects model when heterogeneity (I²) was at least 50% and a random-effects model for substantial heterogeneity (I² > 50%). We evaluated the quality of evidence using the GRADE framework.

MAIN RESULTS

We identified 41 randomised controlled trials (RCTs) involving 194,035 participants from 6422 reports. We assessed studies as having high or unclear risk of bias across multiple domains. Low-quality evidence evidence suggests that providing CVD risk scores may have little or no effect on CVD events compared with usual care (5.4% versus 5.3%; RR 1.01, 95% confidence interval (CI) 0.95 to 1.08; I² = 25%; 3 trials, N = 99,070). Providing CVD risk scores may reduce CVD risk factor levels by a small amount compared with usual care. Providing CVD risk scores reduced total cholesterol (MD -0.10 mmol/L, 95% CI -0.20 to 0.00; I² = 94%; 12 trials, N = 20,437, low-quality evidence), systolic blood pressure (MD -2.77 mmHg, 95% CI -4.16 to -1.38; I² = 93%; 16 trials, N = 32,954, low-quality evidence), and multivariable CVD risk (SMD -0.21, 95% CI -0.39 to -0.02; I² = 94%; 9 trials, N = 9549, low-quality evidence). Providing CVD risk scores may reduce adverse events compared with usual care, but results were imprecise (1.9% versus 2.7%; RR 0.72, 95% CI 0.49 to 1.04; I² = 0%; 4 trials, N = 4630, low-quality evidence). Compared with usual care, providing CVD risk scores may increase new or intensified lipid-lowering medications (15.7% versus 10.7%; RR 1.47, 95% CI 1.15 to 1.87; I² = 40%; 11 trials, N = 14,175, low-quality evidence) and increase new or increased antihypertensive medications (17.2% versus 11.4%; RR 1.51, 95% CI 1.08 to 2.11; I² = 53%; 8 trials, N = 13,255, low-quality evidence).

AUTHORS' CONCLUSIONS

There is uncertainty whether current strategies for providing CVD risk scores affect CVD events. Providing CVD risk scores may slightly reduce CVD risk factor levels and may increase preventive medication prescribing in higher-risk people without evidence of harm. There were multiple study limitations in the identified studies and substantial heterogeneity in the interventions, outcomes, and analyses, so readers should interpret results with caution. New models for implementing and evaluating CVD risk scores in adequately powered studies are needed to define the role of applying CVD risk scores in primary CVD prevention.

Type 2 diabetes: cost-effectiveness of medication adherence and lifestyle interventions

INTRODUCTION

Type 2 diabetes is a major burden for the payer, however, with proper medication adherence, diet and exercise regime, complication occurrence rates, and consequently costs can be altered.

AIMS

The aim of this study was to conduct a cost-effectiveness analysis on real patient data and evaluate which medication adherence or lifestyle intervention is less cost demanding for the payer.

METHODS

Medline was searched systematically for published type 2 diabetes interventions regarding medication adherence and lifestyle in order to determine their efficacies, that were then used in the cost-effectiveness analysis. For cost-effectiveness analysis-required disease progression simulation, United Kingdom Prospective Diabetes Study Outcomes model 2.0 and Slovenian type 2 diabetes patient cohort were used. The intervention duration was set to 1, 2, 5, and 10 years. Complications and drug costs in euro (EUR) were based on previously published type 2 diabetes costs from the Health Care payer perspective in Slovenia.

RESULTS

Literature search proved the following interventions to be effective in type 2 diabetes patients: medication adherence, the Mediterranean diet, aerobic, resistance, and combined exercise. The long-term simulation resulted in no payer net savings. The model predicted following quality-adjusted life-years (QALY) gained and incremental costs for QALY gained (EUR/QALYg) after 10 years of intervention: high-efficacy medication adherence (0.245 QALY; 9,984 EUR/QALYg), combined exercise (0.119 QALY; 46,411 EUR/QALYg), low-efficacy medication adherence (0.075 QALY; 30,967 EUR/QALYg), aerobic exercise (0.069 QALY; 80,798 EUR/QALYg), the Mediterranean diet (0.057 QALY; 27,246 EUR/QALYg), and resistance exercise (0.050 QALY; 111,847 EUR/QALYg).

CONCLUSION

The results suggest that medication adherence intervention is, regarding cost-effectiveness, superior to diet and exercise interventions from the payer perspective. However, the latter could also be utilized by patients without additional costs, but medication adherence intervention requires trained personnel because of its complex structure. Interventions should be performed for >2 years to produce noticeable health/cost results.

Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis

We systematically reviewed randomized controlled trials (RCTs) assessing the effectiveness of computerized decision support systems (CDSSs) featuring rule- or algorithm-based software integrated with electronic health records (EHRs) and evidence-based knowledge. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Abstracts of Reviews of Effects. Information on system design, capabilities, acquisition, implementation context, and effects on mortality, morbidity, and economic outcomes were extracted. Twenty-eight RCTs were included. CDSS use did not affect mortality (16 trials, 37395 patients; 2282 deaths; risk ratio [RR] = 0.96; 95% confidence interval [CI] = 0.85, 1.08; I(2) = 41%). A statistically significant effect was evident in the prevention of morbidity, any disease (9 RCTs; 13868 patients; RR = 0.82; 95% CI = 0.68, 0.99; I(2) = 64%), but selective outcome reporting or publication bias cannot be excluded. We observed differences for costs and health service utilization, although these were often small in magnitude. Across clinical settings, new generation CDSSs integrated with EHRs do not affect mortality and might moderately improve morbidity outcomes.

Stratified patient-centered care in type 2 diabetes: a cluster-randomized, controlled clinical trial of effectiveness and cost-effectiveness

OBJECTIVE

Diabetes treatment should be effective and cost-effective. HbA1c-associated complications are costly. Would patient-centered care be more (cost-) effective if it was targeted to patients within specific HbA1c ranges?

RESEARCH DESIGN AND METHODS

This prospective, cluster-randomized, controlled trial involved 13 hospitals (clusters) in the Netherlands and 506 patients with type 2 diabetes randomized to patient-centered (n=237) or usual care (controls) (n=269). Primary outcomes were change in HbA1c and quality-adjusted life years (QALYs); costs and incremental costs (USD) after 1 year were secondary outcomes. We applied nonparametric bootstrapping and probabilistic modeling over a lifetime using a validated Dutch model. The baseline HbA1c strata were <7.0% (53 mmol/mol), 7.0-8.5%, and >8.5% (69 mmol/mol).

RESULTS

Patient-centered care was most effective and cost-effective in those with baseline HbA1c>8.5% (69 mmol/mol). After 1 year, the HbA1c reduction was 0.83% (95% CI 0.81-0.84%) (6.7 mmol/mol [6.5-6.8]), and the incremental cost-effectiveness ratio (ICER) was 261 USD (235-288) per QALY. Over a lifetime, 0.54 QALYs (0.30-0.78) were gained at a cost of 3,482 USD (2,706-4,258); ICER 6,443 USD/QALY (3,199-9,686). For baseline HbA1c 7.0-8.5% (53-69 mmol/mol), 0.24 QALY (0.07-0.41) was gained at a cost of 4,731 USD (4,259-5,205); ICER 20,086 USD (5,979-34,193). Care was not cost-effective for patients at a baseline HbA1c<7.0% (53 mmol/mol).

CONCLUSIONS

Patient-centered care is more valuable when targeted to patients with HbA1c>8.5% (69 mmol/mol), confirming clinical intuition. The findings support treatment in those with baseline HbA1c 7-8.5% (53-69 mmol/mol) and demonstrate little to no benefit among those with HbA1c<7% (53 mmol/mol). Further studies should assess different HbA1c strata and additional risk profiles to account for heterogeneity among patients.

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

AIMS

To systematically review randomized trials that assessed the effects of computerized clinical decision support systems in ambulatory diabetes management compared with a non-computerized clinical decision support system control.

METHODS

We included all diabetes trials from a comprehensive computerized clinical decision support system overview completed in January 2010, and searched EMBASE, MEDLINE, INSPEC/COMPENDEX and Evidence-Based Medicine Reviews (EBMR) from January 2010 to April 2012. Reference lists of related reviews, included articles and Clinicaltrials.gov were also searched. Randomized controlled trials of patients with diabetes in ambulatory care settings comparing a computerized clinical decision support system intervention with a non-computerized clinical decision support system control, measuring either a process of care or a patient outcome, were included. Screening of studies, data extraction, risk of bias and quality of evidence assessments were carried out independently by two reviewers, and discrepancies were resolved through consensus or third-party arbitration. Authors were contacted for any missing data.

RESULTS

Fifteen trials were included (13 from the previous review and two from the current search). Only one study was at low risk of bias, while the others were of moderate to high risk of bias because of methodological limitations. HbA1c (3 months' follow-up), quality of life and hospitalization (12 months' follow-up) were pooled and all favoured the computerized clinical decision support systems over the control, although none were statistically significant. Triglycerides and practitioner performance tended to favour computerized clinical decision support systems although results were too heterogeneous to pool.

CONCLUSIONS

Computerized clinical decision support systems in diabetes management may marginally improve clinical outcomes, but confidence in the evidence is low because of risk of bias, inconsistency and imprecision.

Cost reductions associated with a quality improvement initiative for patients with ST-elevation myocardial infarction

BACKGROUND

Efforts to reduce door-to-balloon (DTB) times for patients presenting with an ST-elevation myocardial infarction (STEMI) are widespread. Reductions in DTB times have been shown to reduce short-term mortality and decrease inpatient length of stay (LOS) in these high-risk patients. However, there is a limited literature examining the effect that these quality improvement (QI) initiatives have on patient care costs.

METHODS

A STEMI QI program (Cardiac Alert Team [CAT]) initiative was instituted in July 2006 at a single tertiary care medical center located in central Massachusetts. Information was collected on cost data and selected clinical outcomes for consecutively admitted patients with a STEMI. Differences in adjusted hospital costs were compared in three cohorts of patients hospitalized with a STEMI: one before the CAT initiative began (January 2005-June 2006) and two after (October 1, 2007-September 30, 2009, and October 1, 2009-September 30, 2011).

RESULTS

Before the CAT initiative, the average direct inpatient costs related to the care of these patients was $14,634, which decreased to $13,308 (-9.1%) and $13,567 (-7.3%) in the two sequential periods of the study after the CAT initiative was well established. Mean DTB times were 91 minutes before the CAT initiative and were reduced to 55 and 61 minutes in the follow-up periods (p < .001). There was a nonsignificant reduction in LOS from 4.4 days pre-CAT to 3.6 days in both of the post-CAT periods (p = .11).

CONCLUSIONS

A QI program aimed at reducing DTB times for patients with a STEMI also led to a significant reduction in inpatient care costs. The greatest reduction in costs was related to cardiac catheterization, which was not expected and was likely a result of standardization of care and identification of practice inefficiencies.

Computerized decision support systems in primary care for type 2 diabetes patients only improve patients' outcomes when combined with feedback on performance and case management: a systematic review

PURPOSE

Computerized decision support systems (CDSSs) are often part of a multifaceted intervention to improve diabetes care. We reviewed the effects of CDSSs alone or in combination with other supportive tools in primary care for type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

A systematic literature search was conducted for January 1990-July 2011 in PubMed, Embase, and the Cochrane Database and by consulting reference lists. Randomized controlled trials (RCTs) in general practice were selected if the interventions consisted of a CDSS alone or combined with a reminder system and/or feedback on performance and/or case management. The intervention had to be compared with usual care. Two pairs of reviewers independently abstracted all available data. The data were categorized by process of care and patient outcome measures.

RESULTS

Twenty RCTs met inclusion criteria. In 14 studies a CDSS was combined with another intervention. Two studies were left out of the analysis because of low quality. Four studies with a CDSS alone and four studies with a CDSS and reminders showed improvements of the process of care. CDSS with feedback on performance with or without reminders improved the process of care (one study) and patient outcome (two studies). CDSS with case management improved patient outcome (two studies). CDSS with reminders, feedback on performance, and case management improved both patient outcome and the process of care (two studies).

CONCLUSIONS

CDSSs used by healthcare providers in primary T2DM care are effective in improving the process of care; adding feedback on performance and/or case management may also improve patient outcome.

Patients' education, and its impact on care outcomes, resource consumption and working conditions: data from the International Diabetes Management Practices Study (IDMPS)

AIM

To evaluate the impact of diabetes education provided to patients with type 2 diabetes mellitus (T2DM) in non-controlled studies ("real-world conditions") on quality of care, resource consumption and conditions of employment.

METHODS

This cross-sectional study and longitudinal follow-up describe the data (demographic and socioeconomic profiles, clinical characteristics, treatment of hyperglycaemia and associated cardiovascular risk factors, resource consumption) collected during the second phase (2006) of the International Diabetes Management Practices Study (IDMPS). Patients received diabetes education directly from the practice nurse, dietitian or educator, or were referred to ad hoc group-education programmes; all programmes emphasized healthy lifestyle changes, self-care and active participation in disease control and treatment. Educated vs non-educated T2DM patients (n=5692 in each group), paired by age, gender and diabetes duration, were randomly recruited for the IDMPS by participating primary-care physicians from 27 countries in Eastern Europe, Asia, Latin America and Africa. Outcome measures included clinical (body weight, height, waist circumference, blood pressure, foot evaluation), metabolic (HbA(1c) levels, blood lipid profile) and biochemical control measures. Treatment goals were defined according to American Diabetes Association guidelines.

RESULTS

T2DM patients' education significantly improved the percentage of patients achieving target values set by international guidelines. Educated patients increased their insulin use and self-care performance, had a lower rate of chronic complications and a modest increase in cost of care, and probably higher salaries and slightly better productivity.

CONCLUSION

Diabetes education is an efficient tool for improving care outcomes without having a major impact on healthcare costs.

Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review

Background

The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations).

Methods

We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes.

Results

Of 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported.

Conclusions

A small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.

Inhaled insulin forms toxic pulmonary amyloid aggregates

It is well known that interfaces, such as polar-nonpolar or liquid-air, play a key role in triggering protein aggregation in vitro, in particular the aggregation of peptides and proteins with the predisposition of misfolding and aggregation. Here we show that the interface present in the lungs predisposes the lungs to form aggregation of inhaled insulin. Insulin inhalers were introduced, and a large number of diabetic patients have used them. Although inhalers were safe and effective, decreases in pulmonary capacity have been reported in response to inhaled insulin. We hypothesize that the lung air-tissue interface provides a template for the aggregation of inhaled insulin. Our studies were designed to investigate the harmful potential that inhaled insulin has in pulmonary tissue in vivo, through an amyloid formation mechanism. Our data demonstrate that inhaled insulin rapidly forms amyloid in the lungs causing a significant reduction in pulmonary air flow. Our studies exemplify the importance that interfaces play in protein aggregation in vivo, illustrating the potential aggregation of inhaled proteins and the formation of amyloid deposits in the lungs. These insulin deposits resemble the amyloid structures implicated in protein misfolding disorders, such as Alzheimer's and Parkinson's diseases, and could as well be deleterious in nature.