Background: There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice.... Show moreBackground: There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice. We implemented a CDSS to aid general practitioners (GPs) in treating patients with urinary tract infections (UTIs), which are a significant health burden worldwide. Objective: This study aims to prospectively assess the impact of this CDSS on treatment success and change in antibiotic prescription behavior of the physician. In doing so, we hope to identify drivers and obstacles that positively impact the quality of health care practice with ML. Methods: The CDSS was developed by Pacmed, Nivel, and Leiden University Medical Center (LUMC). The CDSS presents the expected outcomes of treatments, using interpretable decision trees as ML classifiers. Treatment success was defined as a subsequent period of 28 days during which no new antibiotic treatment for UTI was needed. In this prospective observational study, 36 primary care practices used the software for 4 months. Furthermore, 29 control practices were identified using propensity score-matching. All analyses were performed using electronic health records from the Nivel Primary Care Database. Patients for whom the software was used were identified in the Nivel database by sequential matching using CDSS use data. We compared the proportion of successful treatments before and during the study within the treatment arm. The same analysis was performed for the control practices and the patient subgroup the software was definitely used for. All analyses, including that of physicians' prescription behavior, were statistically tested using 2-sided z tests with an alpha level of .05. Results: In the treatment practices, 4998 observations were included before and 3422 observations (of 2423 unique patients) were included during the implementation period. In the control practices, 5044 observations were included before and 3360 observations were included during the implementation period. The proportion of successful treatments increased significantly from 75% to 80% in treatment practices (z=5.47, P<.001). No significant difference was detected in control practices (76% before and 76% during the pilot, z=0.02; P=.98). Of the 2423 patients, we identified 734 (30.29%) in the CDSS use database in the Nivel database. For these patients, the proportion of successful treatments during the study was 83%-a statistically significant difference, with 75% of successful treatments before the study in the treatment practices (z=4.95; P<.001). Conclusions: The introduction of the CDSS as an intervention in the 36 treatment practices was associated with a statistically significant improvement in treatment success. We excluded temporal effects and validated the results with the subgroup analysis in patients for whom we were certain that the software was used. This study shows important strengths and points of attention for the development and implementation of an ML-based CDSS in clinical practice. Trial Registration: ClinicalTrials.gov NCT04408976; https://clinicaltrials.gov/ct2/show/NCT04408976 Show less
Background: There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice.... Show moreBackground: There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice. We implemented a CDSS to aid general practitioners (GPs) in treating patients with urinary tract infections (UTIs), which are a significant health burden worldwide.Objective: This study aims to prospectively assess the impact of this CDSS on treatment success and change in antibiotic prescription behavior of the physician. In doing so, we hope to identify drivers and obstacles that positively impact the quality of health care practice with ML.Methods: The CDSS was developed by Pacmed, Nivel, and Leiden University Medical Center (LUMC). The CDSS presents the expected outcomes of treatments, using interpretable decision trees as ML classifiers. Treatment success was defined as a subsequent period of 28 days during which no new antibiotic treatment for UTI was needed. In this prospective observational study, 36 primary care practices used the software for 4 months. Furthermore, 29 control practices were identified using propensity score-matching. All analyses were performed using electronic health records from the Nivel Primary Care Database. Patients for whom the software was used were identified in the Nivel database by sequential matching using CDSS use data. We compared the proportion of successful treatments before and during the study within the treatment arm. The same analysis was performed for the control practices and the patient subgroup the software was definitely used for. All analyses, including that of physicians’ prescription behavior, were statistically tested using 2-sided z tests with an α level of .05.Results: In the treatment practices, 4998 observations were included before and 3422 observations (of 2423 unique patients) were included during the implementation period. In the control practices, 5044 observations were included before and 3360 observations were included during the implementation period. The proportion of successful treatments increased significantly from 75% to 80% in treatment practices (z=5.47, P<.001). No significant difference was detected in control practices (76% before and 76% during the pilot, z=0.02; P=.98). Of the 2423 patients, we identified 734 (30.29%) in the CDSS use database in the Nivel database. For these patients, the proportion of successful treatments during the study was 83%—a statistically significant difference, with 75% of successful treatments before the study in the treatment practices (z=4.95; P<.001).Conclusions: The introduction of the CDSS as an intervention in the 36 treatment practices was associated with a statistically significant improvement in treatment success. We excluded temporal effects and validated the results with the subgroup analysis in patients for whom we were certain that the software was used. This study shows important strengths and points of attention for the development and implementation of an ML-based CDSS in clinical practice. Show less
Background: There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice.... Show moreBackground: There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice. We implemented a CDSS to aid general practitioners (GPs) in treating patients with urinary tract infections (UTIs), which are a significant health burden worldwide.Objective: This study aims to prospectively assess the impact of this CDSS on treatment success and change in antibiotic prescription behavior of the physician. In doing so, we hope to identify drivers and obstacles that positively impact the quality of health care practice with ML.Methods: The CDSS was developed by Pacmed, Nivel, and Leiden University Medical Center (LUMC). The CDSS presents the expected outcomes of treatments, using interpretable decision trees as ML classifiers. Treatment success was defined as a subsequent period of 28 days during which no new antibiotic treatment for UTI was needed. In this prospective observational study, 36 primary care practices used the software for 4 months. Furthermore, 29 control practices were identified using propensity score-matching. All analyses were performed using electronic health records from the Nivel Primary Care Database. Patients for whom the software was used were identified in the Nivel database by sequential matching using CDSS use data. We compared the proportion of successful treatments before and during the study within the treatment arm. The same analysis was performed for the control practices and the patient subgroup the software was definitely used for. All analyses, including that of physicians’ prescription behavior, were statistically tested using 2-sided z tests with an α level of .05.Results: In the treatment practices, 4998 observations were included before and 3422 observations (of 2423 unique patients) were included during the implementation period. In the control practices, 5044 observations were included before and 3360 observations were included during the implementation period. The proportion of successful treatments increased significantly from 75% to 80% in treatment practices (z=5.47, P<.001). No significant difference was detected in control practices (76% before and 76% during the pilot, z=0.02; P=.98). Of the 2423 patients, we identified 734 (30.29%) in the CDSS use database in the Nivel database. For these patients, the proportion of successful treatments during the study was 83%—a statistically significant difference, with 75% of successful treatments before the study in the treatment practices (z=4.95; P<.001).Conclusions: The introduction of the CDSS as an intervention in the 36 treatment practices was associated with a statistically significant improvement in treatment success. We excluded temporal effects and validated the results with the subgroup analysis in patients for whom we were certain that the software was used. This study shows important strengths and points of attention for the development and implementation of an ML-based CDSS in clinical practice. Show less
Background Implementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal... Show moreBackground Implementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal dilemmas. This study aimed to identify challenges regarding responsibility and liability when prescribing digital health in clinical practice. This was part of an overarching project aiming to explore the most pressing ethical and legal obstacles regarding the implementation and adoption of digital health in the Netherlands, and to propose actionable solutions. Methods A series of multidisciplinary focus groups with stakeholders who have relevant digital health expertise were analysed through thematic analysis. Results The emerging general theme was 'uncertainty regarding responsibilities' when adopting digital health. Key dilemmas take place in clinical settings and within the doctor-patient relationship ('professional digital health'). This context is particularly challenging because different stakeholders interact. In the absence of appropriate legal frameworks and codes of conduct tailored to digital health, physicians' responsibility is to be found in their general duty of care. In other words: to do what is best for patients (not causing harm and doing good). Professional organisations could take a leading role to provide more clarity with respect to physicians' responsibility, by developing guidance describing physicians' duty of care in the context of digital health, and to address the resulting responsibilities. Conclusions Although legal frameworks governing medical practice describe core ethical principles, rights and obligations of physicians, they do not suffice to clarify their responsibilities in the setting of professional digital health. Here we present a series of recommendations to provide more clarity in this respect, offering the opportunity to improve quality of care and patients' health. The recommendations can be used as a starting point to develop professional guidance and have the potential to be adapted to other healthcare professionals and systems. Show less
Silven, A.V.; Peet, P.G. van; Boers, S.N.; Tabak, M.; Groot, A. de; Hendriks, D.; ... ; Villalobos-Quesada, M. 2022
BackgroundImplementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal dilemmas... Show moreBackgroundImplementation of digital health (eHealth) generally involves adapting pre-established and carefully considered processes or routines, and still raises multiple ethical and legal dilemmas. This study aimed to identify challenges regarding responsibility and liability when prescribing digital health in clinical practice. This was part of an overarching project aiming to explore the most pressing ethical and legal obstacles regarding the implementation and adoption of digital health in the Netherlands, and to propose actionable solutions.MethodsA series of multidisciplinary focus groups with stakeholders who have relevant digital health expertise were analysed through thematic analysis.ResultsThe emerging general theme was ‘uncertainty regarding responsibilities’ when adopting digital health. Key dilemmas take place in clinical settings and within the doctor-patient relationship (‘professional digital health’). This context is particularly challenging because different stakeholders interact. In the absence of appropriate legal frameworks and codes of conduct tailored to digital health, physicians’ responsibility is to be found in their general duty of care. In other words: to do what is best for patients (not causing harm and doing good). Professional organisations could take a leading role to provide more clarity with respect to physicians’ responsibility, by developing guidance describing physicians’ duty of care in the context of digital health, and to address the resulting responsibilities.ConclusionsAlthough legal frameworks governing medical practice describe core ethical principles, rights and obligations of physicians, they do not suffice to clarify their responsibilities in the setting of professional digital health. Here we present a series of recommendations to provide more clarity in this respect, offering the opportunity to improve quality of care and patients’ health. The recommendations can be used as a starting point to develop professional guidance and have the potential to be adapted to other healthcare professionals and systems. Show less
Buul, A.R. van; Kasteleyn, M.J.; Poberezhets, V.; Bonten, T.N.; Mutsert, R. de; Hiemstra, P.S.; ... ; Taube, C. 2022
Physical inactivity is already present among patients with chronic obstructive pulmonary disease (COPD) of mild or moderate airflow obstruction. Most previous studies that reported on determinants... Show morePhysical inactivity is already present among patients with chronic obstructive pulmonary disease (COPD) of mild or moderate airflow obstruction. Most previous studies that reported on determinants of physical activity in COPD included patients with severe COPD. Therefore, this study aimed to explore which patient characteristics were related to physical activity in COPD patients with mild or moderate airflow obstruction. Cross-sectional analyses were performed on patients selected from the population-based Netherlands Epidemiology of Obesity study. Patients were included if they had a physician-diagnosed COPD GOLD 0-2 or had newly diagnosed COPD GOLD 1-2. Physical activity was evaluated using the Short Questionnaire to Assess Health-Enhancing Physical Activity (SQUASH) questionnaire and reported in hours per week of metabolic equivalents (MET-h/week). Associations between sociodernographic, lifestyle, clinical and functional characteristics were examined using regression analysis. 323 patients were included in research (77 with physician-diagnosed and 246 with newly diagnosed COPD). We found that physical activity was positively associated with pulmonary function: FEV1 (regression coefficient 0.40 (95% CI 0.09,0.71)) and FVC (regression coefficient 0.34 (95% CI 0.06,0.61)). Physical activity was associated with anxiety (regression coefficient -0.9 (95% CI 0.3,1.6)) only for physician-diagnosed patients. Lung function and anxiety level determine the level of physical activity among COPD patients with mild or moderate airflow obstruction. Thus, adjusting physical activity plans accordingly could help to increase physical activity level of the patients. Show less
BackgroundStructured primary diabetes care within a collectively supported setting is associated with better monitoring of biomedical and lifestyle-related target indicators among people with type... Show moreBackgroundStructured primary diabetes care within a collectively supported setting is associated with better monitoring of biomedical and lifestyle-related target indicators among people with type 2 diabetes and with better HbA1c levels. Whether socioeconomic status affects delivery of care in terms of monitoring and its association with HbA1c levels within this approach, is unclear. This study aims to understand whether, within a structured care approach, 1) socioeconomic categories differ concerning diabetes monitoring as recommended; 2) socioeconomic status modifies the association between monitoring as recommended and HbA1c.MethodsObservational real-life cohort study with primary care registry data from general practitioners within diverse socioeconomic areas, who are supported with implementation of structured diabetes care. People with type 2 diabetes mellitus were offered quarterly diabetes consultations. 'Monitoring as recommended' by professional guidelines implied minimally one annual registration of HbA1c, systolic blood pressure, LDL, BMI, smoking behaviour and physical activity. Regarding socioeconomic status, deprived, advantageous urban and advantageous suburban categories were compared to the intermediate category concerning 1) recommended monitoring; 2) association between recommended monitoring and HbA1c.ResultsAim 1 (n=13,601 people): Compared to the intermediate socioeconomic category, no significant differences in odds of being monitored as recommended were found in the deprived (OR 0.45 (95%CI 0.19-1.08)), advantageous-urban (OR 1.27 (95%CI 0.46-3.54)) and advantageous- suburban (OR 2.32 (95%CI 0.88-6.08)) categories. Aim 2 (n=11,164 people): People with recommended monitoring had significantly lower HbA1c levels than incompletely-monitored people (-2.4 (95%CI -2.9;-1.8)mmol/mol). SES modified monitoring-related HbA1c differences, which were significantly higher in the deprived (-3.3 (95%CI -4.3;-2.4)mmol/mol) than the intermediate category (-1.3 (95%CI -2.2;-0.4)mmol/mol). Conclusions Within a structured diabetes care setting, socioeconomic status is not associated with recommended monitoring. Socioeconomic differences in the association between recommended monitoring and HbA1c levels advocate further exploration of practice and patient-related factors contributing to appropriate monitoring and for care adjustment to population needs. Show less
Background Structured primary diabetes care within a collectively supported setting is associated with better monitoring of biomedical and lifestyle-related target indicators amongst people with... Show moreBackground Structured primary diabetes care within a collectively supported setting is associated with better monitoring of biomedical and lifestyle-related target indicators amongst people with type 2 diabetes and with better HbA1c levels. Whether socioeconomic status affects the delivery of care in terms of monitoring and its association with HbA1c levels within this approach, is unclear. This study aims to understand whether, within a structured care approach, (1) socioeconomic categories differ concerning diabetes monitoring as recommended; (2) socioeconomic status modifies the association between monitoring as recommended and HbA1c.Methods Observational real-life cohort study with primary care registry data from general practitioners within diverse socioeconomic areas, who are supported with the implementation of structured diabetes care. People with type 2 diabetes mellitus were offered quarterly diabetes consultations. "Monitoring as recommended" by professional guidelines implied minimally one annual registration of HbA1c, systolic blood pressure, LDL, BMI, smoking behaviour and physical activity. Regarding socioeconomic status, deprived, advantageous urban and advantageous suburban categories were compared to the intermediate category concerning (a) recommended monitoring; (b) association between recommended monitoring and HbA1c.Results Aim 1 (n = 13 601 people): Compared to the intermediate socioeconomic category, no significant differences in odds of being monitored as recommended were found in the deprived (OR 0.45 (95% CI 0.19-1.08)), advantageous urban (OR 1.27 (95% CI 0.46-3.54)) and advantageous suburban (OR 2.32 (95% CI 0.88-6.08)) categories. Aim 2 (n = 11 164 people): People with recommended monitoring had significantly lower HbA1c levels than incompletely monitored people (-2.4 (95% CI -2.9; -1.8) mmol/mol). SES modified monitoring-related HbA1c differences, which were significantly higher in the deprived (-3.3 (95% CI -4.3; -2.4) mmol/mol) than the intermediate category (-1.3 (95% CI -2.2; -0.4) mmol/mol).Conclusions Within a structured diabetes care setting, socioeconomic status is not associated with recommended monitoring. Socioeconomic differences in the association between recommended monitoring and HbA1c levels advocate further exploration of practice and patient-related factors contributing to appropriate monitoring and for care adjustment to population needs. Show less
Bonten, T.N.; Verkleij, S.M.; Kleij, R.M.J.J. van der; Busch, K.; Hout, W.B. van den; Chavannes, N.H.; Numans, M.E. 2021
Introduction Lifestyle interventions are shown to be effective in improving cardiovascular disease (CVD) risk factors. It has been suggested that general practitioners can play an essential role in... Show moreIntroduction Lifestyle interventions are shown to be effective in improving cardiovascular disease (CVD) risk factors. It has been suggested that general practitioners can play an essential role in CVD prevention. However, studies into lifestyle interventions for primary care patients at high cardiovascular risk are scarce and structural implementation of lifestyle interventions can be challenging. Therefore, this study aims to (1) evaluate (cost-)effectiveness of implementation of an integrated group-based lifestyle programme in primary care practices; (2) identify effective intervention elements and (3) identify implementation determinants of an integrated group-based lifestyle intervention for patients with high cardiovascular risk. Methods and analysis The Healthy Heart study is a non-randomised cluster stepped-wedge trial. Primary care practices will first offer standard care during a control period of 2-6 months, after which practices will switch (step) to the intervention, offering participants a choice between a group-based lifestyle programme or standard care. Participants enrolled during the control period (standard care) will be compared with participants enrolled during the intervention period (combined standard care and group-based lifestyle intervention). We aim to include 1600 primary care patients with high cardiovascular risk from 55 primary care practices in the area of The Hague, the Netherlands. A mixed-methods process evaluation will be used to simultaneously assess effectiveness and implementation outcomes. The primary outcome measure will be achievement of individual lifestyle goals after 6 months. Secondary outcomes include lifestyle change of five lifestyle components (smoking, alcohol consumption, diet, weight and physical activity) and improvement of quality of life and self-efficacy. Outcomes are assessed using validated questionnaires at baseline and 3, 6, 12 and 24 months of follow-up. Routine care data will be used to compare blood pressure and cholesterol levels. Cost-effectiveness of the lifestyle intervention will be evaluated. Implementation outcomes will be assessed using the RE-AIM model, to assesses five dimensions of implementation at different levels of organisation: reach, efficacy, adoption, implementation and maintenance. Determinants of adoption and implementation will be assessed using focus groups consisting of professionals and patients. Ethics and dissemination This study is approved by the Ethics Committee of the Leiden University Medical Center (P17.079). Results will be shared with the primary care group, healthcare providers and patients, and will be disseminated through journal publications and conference presentations. Show less
Background Education is essential to the integration of eHealth into primary care, but eHealth is not yet embedded in medical education. Objectives In this opinion article, we aim to support... Show moreBackground Education is essential to the integration of eHealth into primary care, but eHealth is not yet embedded in medical education. Objectives In this opinion article, we aim to support organisers of Continuing Professional Development (CPD) and teachers delivering medical vocational training by providing recommendations for eHealth education. First, we describewhatis required to help primary care professionals and trainees learn about eHealth. Second, we elaborate onhoweHealth education might be provided. Discussion We consider four essential topics. First, an understanding of existing evidence-based eHealth applications and conditions for successful development and implementation. Second, required digital competencies of providers and patients. Third, how eHealth changes patient-provider and provider-provider relationships and finally, understanding the handling of digital data. Educational activities to address these topics include eLearning, blended learning, courses, simulation exercises, real-life practice, supervision and reflection, role modelling and community of practice learning. More specifically, a CanMEDS framework aimed at defining curriculum learning goals can support eHealth education by describing roles and required competencies. Alternatively, Kern's conceptual model can be used to design eHealth training programmes that match the educational needs of the stakeholders using eHealth. Conclusion Vocational and CPD training in General Practice needs to build on eHealth capabilities now. We strongly advise the incorporation of eHealth education into vocational training and CPD activities, rather than providing it as a separate single module. How learning goals and activities take shape and how competencies are evaluated clearly requires further practice, evaluation and study. Show less
Despite significant efforts, the COVID-19 pandemic has put enormous pressure on health care systems around the world, threatening the quality of patient care. Telemonitoring offers the opportunity... Show moreDespite significant efforts, the COVID-19 pandemic has put enormous pressure on health care systems around the world, threatening the quality of patient care. Telemonitoring offers the opportunity to carefully monitor patients with a confirmed or suspected case of COVID-19 from home and allows for the timely identification of worsening symptoms. Additionally, it may decrease the number of hospital visits and admissions, thereby reducing the use of scarce resources, optimizing health care capacity, and minimizing the risk of viral transmission. In this paper, we present a COVID-19 telemonitoring care pathway developed at a tertiary care hospital in the Netherlands, which combined the monitoring of vital parameters with video consultations for adequate clinical assessment. Additionally, we report a series of medical, scientific, organizational, and ethical recommendations that may be used as a guide for the design and implementation of telemonitoring pathways for COVID-19 and other diseases worldwide. Show less
Background: Despite the increase in use and high expectations of digital health solutions, scientific evidence about the effectiveness of electronic health (eHealth) and other aspects such as... Show moreBackground: Despite the increase in use and high expectations of digital health solutions, scientific evidence about the effectiveness of electronic health (eHealth) and other aspects such as usability and accuracy is lagging behind eHealth solutions are complex interventions, which require a wide array of evaluation approaches that are capable of answering the many different questions that arise during the consecutive study phases of eHealth development and implementation. However, evaluators seem to struggle in choosing suitable evaluation approaches in relation to a specific study phase.Objective: The objective of this project was to provide a structured overview of the existing eHealth evaluation approaches, with the aim of assisting eHealth evaluators in selecting a suitable approach for evaluating their eHealth solution at a specific evaluation study phase.Methods: Three consecutive steps were followed. Step 1 was a systematic scoping review, summarizing existing eHealth evaluation approaches. Step 2 was a concept mapping study asking eHealth researchers about approaches for evaluating eHealth. In step 3, the results of step 1 and 2 were used to develop an "eHealth evaluation cycle" and subsequently compose the online "eHealth methodology guide."Results: The scoping review yielded 57 articles describing 50 unique evaluation approaches. The concept mapping study questioned 43 eHealth researchers, resulting in 48 unique approaches. After removing duplicates, 75 unique evaluation approaches remained. Thereafter, an "eHealth evaluation cycle" was developed, consisting of six evaluation study phases: conceptual and planning, design, development and usability, pilot (feasibility), effectiveness (impact), uptake (implementation), and all phases. Finally, the "eHealth methodology guide" was composed by assigning the 75 evaluation approaches to the specific study phases of the "eHealth evaluation cycle."Conclusions: Seventy-five unique evaluation approaches were found in the literature and suggested by eHealth researchers, which served as content for the online "eHealth methodology guide." By assisting evaluators in selecting a suitable evaluation approach in relation to a specific study phase of the "eHealth evaluation cycle," the guide aims to enhance the quality, safety, and successful long-term implementation of novel eHealth solutions. Show less
Objective Whether care group participation by general practitioners improves delivery of diabetes care is unknown. Using 'monitoring of biomedical and lifestyle target indicators as recommended by... Show moreObjective Whether care group participation by general practitioners improves delivery of diabetes care is unknown. Using 'monitoring of biomedical and lifestyle target indicators as recommended by professional guidelines' as an operationalisation for quality of care, we explored whether (1) in new practices monitoring as recommended improved a year after initial care group participation (aim 1); (2) new practices and experienced practices differed regarding monitoring (aim 2).Design Observational, real-life cohort study.Setting Primary care registry data from Eerstelijns Zorggroep Haaglanden (ELZHA) care group.Participants Aim 1: From six new practices (n=538 people with diabetes) that joined care group ELZHA in January 2014, two practices (n=211 people) were excluded because of missing baseline data; four practices (n=182 people) were included. Aim 2: From all six new practices (n=538 people), 295 individuals were included. From 145 experienced practices (n=21 465 people), 13 744 individuals were included.Exposure Care group participation includes support by staff nurses on protocolised diabetes care implementation and availability of a system providing individual monitoring information. 'Monitoring as recommended' represented minimally one annual registration of each biomedical (HbA1c, systolic blood pressure, low-density lipoprotein) and lifestyle-related target indicator (body mass index, smoking behaviour, physical exercise).Primary outcome measures Aim 1: In new practices, odds of people being monitored as recommended in 2014 were compared with baseline (2013). Aim 2: Odds of monitoring as recommended in new and experienced practices in 2014 were compared.Results Aim 1: After 1-year care group participation, odds of being monitored as recommended increased threefold (OR 3.00, 95% CI 1.84 to 4.88, p<0.001). Aim 2: Compared with new practices, no significant differences in the odds of monitoring as recommended were found in experienced practices (OR 1.21, 95% CI 0.18 to 8.37, p=0.844).Conclusions We observed a sharp increase concerning biomedical and lifestyle monitoring as recommended after 1-year care group participation, and subsequently no significant difference between new and experienced practices-indicating that providing diabetes care within a collective approach rapidly improves registration of care. Show less
