Dataset Information

Assessment of the Adjusted Clinical Groups system in Dutch primary care using electronic health records: a retrospective cross-sectional study.

ABSTRACT:

Background

Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners' (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP healthcare utilization.

Methods

To reach our aim, two variables of the ACG System, the Aggregated Diagnosis Groups (ADG) and the mutually exclusive ACG categories were explored. The population for this pilot analysis consisted of 23,618 persons listed with five participating general practices within one region in the Netherlands. ACG analyses were performed based on historical Electronic Health Records data from 2014 consisting of primary care diagnoses and pharmaceutical data. Logistic regression models were estimated and AUC's were calculated to explore the diagnostic value of the models including ACGs and ADGs separately with GP healthcare utilization as the dependent variable. The dependent variable was categorized using four different cut-off points: zero, one, two and three visits per year.

Results

The ACG and ADG models performed as well as models using International Classification of Primary Care chapters, regarding the association with GP utilization. AUC values were between 0.79 and 0.85. These models performed better than the base model (age and gender only) which showed AUC values between 0.64 and 0.71.

Conclusion

The results of this study show that the ACG system is a useful tool to stratify Dutch primary care populations with GP healthcare utilization as the outcome variable.

SUBMITTER: Girwar SM

PROVIDER: S-EPMC7945308 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Assessment of the Adjusted Clinical Groups system in Dutch primary care using electronic health records: a retrospective cross-sectional study.

Girwar Shelley-Ann M SM Fiocco Marta M Sutch Stephen P SP Numans Mattijs E ME Bruijnzeels Marc A MA

BMC health services research 20210310 1

<h4>Background</h4>Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners' (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model cat ...[more]

PMID: 33691681

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Project description:BACKGROUND: In view of rapidly increasing prescription costs, case-mix adjustment should be considered for effective control of costs. We have estimated the variability in pharmacy costs explained by ACG in centers using patient electronic records, profiled centers and physicians and analyzed the correlation between cost and quality of prescription. METHODS: We analyzed 65,630 patient records attending five primary care centers in Spain during 2005. Variables explored were age, gender, registered diagnosed episodes of care during 2005, total cost of prescriptions, physician and center. One ACG was assigned to each patient with ACG case-mix software version 7.1. In a two-part model, logistic regression was used to explain the incurrence of drug expenditure at the first stage and a linear mixed model that considered the multilevel structure of data modeled the cost, conditional upon incurring any expense. Risk and efficiency indexes in pharmacy cost adjusted for ACG were obtained for centers and physicians. Spearman rank correlation between physician expenditure, adjusted for ACG, and a prescription quality index was also obtained. Pediatric and adult data were analyzed separately. RESULTS: No prescription was recorded for 13% of adults and 39.6% of children. The proportion of variance of the incurrence of expenditure explained by ACGs was 0.29 in adults and 0.21 in children. For adults with prescriptions, the variance of cost explained by ACGs was 35.4%, by physician-center was 1.8% and age 10.5% (residual 52.3%). For children, ACGs explained 22.4% of cost and physician-center 10.9% (residual 66.7%). Center efficiency index for adults ranged 0.58 to 1.22 and for children 0.32 to 2.36. Spearman correlation between expenditure and prescription quality index was -0.36 in family physicians (p = 0.019, N = 41) and -0.52 in pediatricians (p = 0.08, N = 12). CONCLUSION: In our setting, ACG is the variable studied that explains more variability in pharmacy cost in adults compared to physician and center. In children there is greater variability among physicians and centers not related to case-mix. In our sites, ACG is useful to profile physicians and centers using electronic records in real practical conditions. Physicians with lower pharmaceutical expenditure have higher scores for a prescription quality index.

Project description:IntroductionThe extent of preventable medication-related hospital admissions and medication-related issues in primary care is significant enough to justify developing decision support systems for medication safety surveillance. The prerequisite for such systems is defining a relevant set of medication safety-related indicators and understanding the influence of both patient and general practice characteristics on medication prescribing and monitoring.ObjectiveThe aim of the study was to investigate the feasibility of linked primary and secondary care electronic health record data for surveillance of medication safety, examining not only prescribing but also monitoring, and associations with patient- and general practice-level characteristics.MethodsA cross-sectional study was conducted using linked records of patients served by one hospital and over 50 general practices in Salford, UK. Statistical analysis consisted of mixed-effects logistic models, relating prescribing safety indicators to potential determinants.ResultsThe overall prevalence (proportion of patients with at least one medication safety hazard) was 5.45 % for prescribing indicators and 7.65 % for monitoring indicators. Older patients and those on multiple medications were at higher risk of prescribing hazards, but at lower risk of missed monitoring. The odds of missed monitoring among all patients were 25 % less for males, 50 % less for patients in practices that provide general practitioner training, and threefold higher in practices serving the most deprived compared with the least deprived areas. Practices with more prescribing hazards did not tend to show more monitoring issues.ConclusionsSystematic collection, collation, and analysis of linked primary and secondary care records produce plausible and useful information about medication safety for a health system. Medication safety surveillance systems should pay close attention to patient age and polypharmacy with respect to both prescribing and monitoring failures; treat prescribing and monitoring as different statistical processes, rather than a combined measure of prescribing safety; and audit the socio-economic equity of missed monitoring.

Project description:OBJECTIVES:It is unclear why the use of email consultation is not more widespread in Dutch general practice, particularly because, since 2006, its costs can be reimbursed. To encourage further implementation, it is needed to understand the current use of email consultations. This study aims to understand the use of email consultation by different patient groups, compared with other general practice (GP) consultations. SETTING:For this retrospective observational study, we used Dutch routine electronic health record data obtained from NIVEL Primary Care Database for the years 2010 and 2014. PARTICIPANTS:200 general practices were included in 2010 (734 122 registered patients) and 434 in 2014 (1 630 386 registered patients). PRIMARY OUTCOME MEASURES:The number and percentage of email consultations and patient characteristics (age, gender, neighbourhood socioeconomic status and diagnoses) of email consultation users were investigated and compared with those who had a telephone or face-to-face consultation. General practice characteristics were also taken into account. RESULTS:32.0% of the Dutch general practices had at least one email consultation in 2010, rising to 52.8% in 2014. In 2014, only 0.7% of the GP consultations were by email (the others comprised home visits, telephone and face-to-face consultations). Its use highly varied among general practices. Most email consultations were done for psychological (14.7%); endocrine, metabolic and nutritional (10.9%); and circulatory (10.7%) problems. These diagnosis categories appeared less frequently in telephone and face-to-face consultations. Patients who had an email consultation were older than patients who had a telephone or face-to-face consultation. In contrast, patients with diabetes who had an email consultation were younger. CONCLUSION:Even though email consultation was done in half the general practices in the Netherlands in 2014, the actual use of it is extremely low. Patients who had an email consultation differ from those who had a telephone or face-to-face consultation. In addition, the use of email consultation by patients is dependent on its provision by GPs.

Project description:ObjectiveTo determine common patterns of recorded primary care for osteoarthritis (OA), and patient and provider characteristics associated with the quality of recorded care.DesignAn observational study nested within a cluster-randomised controlled trial.SettingEight UK general practices who were part of the Management of Osteoarthritis in Consultations study.ParticipantsPatients recorded as consulting within the eight general practices for clinical OA.Primary outcomesAchievement of seven quality indicators of care (pain/function assessment, information provision, exercise/weight advice, analgesics, physiotherapy), recorded through an electronic template or routinely recorded in the electronic healthcare records, was identified for patients aged ≥45 years consulting over a 6-month period with clinical OA. Latent class analysis was used to cluster patients based on care received. Clusters were compared on patient and clinician-level characteristics.Results1724 patients (median by practice 183) consulted with clinical OA. Common patterns of recorded quality care were: cluster 1 (38%, High) received most quality indicators of care; cluster 2 (11%, Moderate) had pain and function assessment, and received or were considered for other indicators; cluster 3 (17%, Low) had pain and function assessment, and received or were considered for paracetamol or topical non-steroidal anti-inflammatory drugs; cluster 4 (35%, None) had no recorded quality indicators. Patients with higher levels of recorded care consulted a clinician who saw more patients with OA, consulted multiple times and had less morbidity. Those in the High cluster were more likely to have recorded diagnosed OA and have knee/hip OA.ConclusionsPatterns of recorded care for OA fell into four natural clusters. Appropriate delivery of core interventions and relatively safe pharmacological options for OA are still not consistently recorded as provided in primary care. Further research to understand clinical recording behaviours and determine potential barriers to quality care alongside effective training for clinicians is needed.Trial registration numberISRCTN06984617; Results.

Project description:BackgroundIn recent years, most Dutch general practitioners started working under the umbrella of diabetes care groups, responsible for the organisation and coordination of diabetes care. The quality management of these new organisations receives growing interest, although its association with quality of diabetes care is yet unclear. The best way to measure quality management is unknown and it has not yet been studied at the level of outpatient clinics or care groups. We aimed to assess quality management of type 2 diabetes care in care groups and outpatient clinics.ResultsQuality management was measured with online questionnaires, containing six domains (see below). They were divided into 28 subdomains, with 59 (care groups) and 57 (outpatient clinics) questions respectively. The mean score of the domains reflects the overall score (0-100%) of an organisation. Two quality managers of all Dutch care groups and outpatient clinics were invited to fill out the questionnaire.Sixty care groups (response rate 61.9%) showed a mean score of 59.6% (CI 57.1-62.1%). The average score in 52 outpatient clinics (response rate 50.0%) was 61.9% (CI 57.5-66.8%).Mean scores on the six domains for care groups and outpatient clinics respectively were: 'organisation of care' 71.9% (CI 68.8-74.9%), 76.8% (CI 72.8-80.7%); 'multidisciplinary teamwork' 67.1% (CI 62.4-71.9%), 71.5% (CI 65.3-77.8%); 'patient centeredness' 46.7% (CI 42.6-50.7%), 62.5% (CI 57.7-67.2%); 'performance management' 63.3% (CI 61.2-65.3%), 50.9% (CI 44.2-57.5%); 'quality improvement policy' 52.6% (CI 49.2-56.1%), 50.9% (CI 44.6-57.3%); and 'management strategies' 56.0% (CI 51.4-60.7%), 59.0% (CI 52.8-65.2%). On subdomains, care groups scored highest on 'care program' (83.3%) and 'measured outcomes' (98.3%) and lowest on 'patient safety' (15.1%) and 'patient involvement' (17.7%). Outpatient clinics scored high on the presence of a 'diabetic foot team' (81.6%) and the support in 'self-management' (81.0%) and low on 'patient involvement' (26.8%) and 'inspection of medical file' (28.0%).ConclusionsThis nationwide assessment reveals that the level of quality management in diabetes care varies between several subdomains in both diabetes care groups and outpatient clinics.

Project description:BackgroundPostoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information.ObjectiveThe objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach.MethodsIn our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type.ResultsIn the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables.ConclusionsA machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery.

Project description:BackgroundElectronic personal health records (ePHRs) are secure Web-based tools that enable individuals to access, manage, and share their medical records. England recently introduced a nationwide ePHR called Patient Online. As with ePHRs in other countries, adoption rates of Patient Online remain low. Understanding factors affecting patients' ePHR use is important to increase adoption rates and improve the implementation success of ePHRs.ObjectiveThis study aimed to examine factors associated with patients' use of ePHRs in England.MethodsThe unified theory of acceptance and use of technology was adapted to the use of ePHRs. To empirically examine the adapted model, a cross-sectional survey of a convenience sample was carried out in 4 general practices in West Yorkshire, England. Factors associated with the use of ePHRs were explored using structural equation modeling.ResultsOf 800 eligible patients invited to take part in the survey, 624 (78.0%) returned a valid questionnaire. Behavioral intention (BI) was significantly influenced by performance expectancy (PE; beta=.57, P<.001), effort expectancy (EE; beta=.16, P<.001), and perceived privacy and security (PPS; beta=.24, P<.001). The path from social influence to BI was not significant (beta=.03, P=.18). Facilitating conditions (FC) and BI significantly influenced use behavior (UB; beta=.25, P<.001 and beta=.53, P<.001, respectively). PE significantly mediated the effect of EE and PPS on BI (beta=.19, P<.001 and beta=.28, P=.001, respectively). Age significantly moderated 3 paths: PE→BI, EE→BI, and FC→UB. Sex significantly moderated only the relationship between PE and BI. A total of 2 paths were significantly moderated by education and internet access: EE→BI and FC→UB. Income moderated the relationship between FC and UB. The adapted model accounted for 51% of the variance in PE, 76% of the variance in BI, and 48% of the variance in UB.ConclusionsThis study identified the main factors that affect patients' use of ePHRs in England, which should be taken into account for the successful implementation of these systems. For example, developers of ePHRs should involve patients in the process of designing the system to consider functions and features that fit patients' preferences and skills to ensure systems are useful and easy to use. The proposed model accounted for 48% of the variance in UB, indicating the existence of other, as yet unidentified, factors that influence the adoption of ePHRs. Future studies should confirm the effect of the factors included in this model and identify additional factors.

Dataset Information

Assessment of the Adjusted Clinical Groups system in Dutch primary care using electronic health records: a retrospective cross-sectional study.

Background

Methods

Results

Conclusion

Publications

Assessment of the Adjusted Clinical Groups system in Dutch primary care using electronic health records: a retrospective cross-sectional study.

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