Adjusted Clinical Groups System Research Articles

Identifying individuals with complex and long-term health-care needs using the Johns Hopkins Adjusted Clinical Groups System: A comparison of data from primary and specialist health care

This study aimed to present the Johns Hopkins Adjusted Clinical Groups (ACG) System risk stratification profile of a total adult population of somatic health-care users when using data from either general practitioners (GPs) or hospital services and to compare the number and characteristics of individuals identified as having complex and long-term health-care needs in each data source. This was a registry-based study that included all adult residents (N=168,285) in four municipalities in Central Norway who received somatic health care during 2013. Risk profiles were generated using the ACG System based on age, sex and diagnoses registered by GPs or the local hospital. ACG output variables on number of chronic conditions, frailty and concurrent resource utilisation were chosen as indicators of complexity. Nearly nine out of 10 (83.9%) of the population had been in contact with a GP, and 35.4% with the hospital. The mean number of diagnoses (3.0) was equal in both sources. A larger proportion of the population had higher risk scores in all variables except frailty when comparing hospital data to GP data. This was also found when comparing individuals identified as having complex and long-term health-care needs. A similar proportion of the population was found to have complex and long-term health-care needs (hospital 6.7%, GP 6.3%), but only one in five (21.5%) were identified in both data sets. As data from GPs and hospitals identified mostly different individuals with complex and long-term health-care needs, combining data sources is likely to be the best option for identifying those most in need of special attention.

Comparing and validating medication complexity from insurance claims against electronic health records.

BACKGROUND: Patient effort to comply with complex medication instructions is known to be related to nonadherence and subsequent medical complications or health care costs. A widely used Medication Regimen Complexity Index (MRCI) has been used with electronic health records (EHRs) to identify patients who could benefit from pharmacist intervention. A similar claims-derived measure may be better suited for clinical decision support, since claims offer a more complete view of patient care and health utilization. OBJECTIVE: To define and validate a novel insurance claims-based medication complexity score (MCS) patterned after the widely used MRCI, derived from EHRs. METHODS: Insurance claims and EHR data were provided by HealthPartners (N = 54,988) (Bloomington, Minnesota) and The Johns Hopkins Health System (N = 28,589) (Baltimore, Maryland) for years 2013 and 2017, respectively. Yearly measures of medication complexity were developed for each patient and evaluated with one another using rank correlation within different clinical subgroupings. Indicators for the presence of individually complex prescriptions were also developed and assessed using exact agreement. Complexity measures were then correlated with select covariates to further validate the concordance between MCS and MRCI with respect to clinical metrics. These included demographic, comorbidity, and health care utilization markers. Prescribed medications in each system's EHR were coded using the previously validated MRCI weighting rules. Insurance claims for retail pharmacy medications were coded using our novel MCS, which closely followed MRCI scoring rules. RESULTS: EHR-based MRCI and claims-based MCS were significantly correlated with one another for most clinical subgroupings. Likewise, both measures were correlated with several covariates, including count of active medications and chronic conditions. The MCS was, in most cases, more associated with key health covariates than was MRCI, although both were consistently significant. We found that the highest correlation between MCS and MRCI is obtained with patients who have similar counts of pharmacy records between EHRs and claims (HealthPartners: P = 0.796; Johns Hopkins Health System: P = 0.779). CONCLUSIONS: The findings suggest good correspondence between MCS and MRCI and that claims data represent a useful resource for assessing medication complexity. Claims data also have major practical advantages, such as interoperability across health care systems, although they lack the detailed clinical context of EHRs. DISCLOSURES: The Johns Hopkins University holds the copyright to the Adjusted Clinical Groups (ACG) system and receives royalties from the global distribution of the ACG system. This revenue supports a portion of the authors' salary. No additional or external funding supported this work. The authors have no conflict of interest to disclose.

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

BackgroundWithin 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.MethodsTo 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.ResultsThe 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.ConclusionThe 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.

Health care service use and costs for a cohort of high-needs elderly diabetic patients

AimsTo describe the impact of diabetes comorbidities on the health care services use and costs of a cohort of elderly patients with diabetes and high health care needs (HHCN), based on real-world data. MethodsWe focused on a cohort of diabetic patients with HHCN belonging to Resource Utilization Bands 4 and 5 according to the Adjusted Clinical Group (ACG) system. Their comorbidities were assessed using the clinical diagnoses that the ACG system assigns to single patients by combining different information flows. Regression models were applied to analyze the associations between comorbidities and health care service use or costs, adjusting for age and sex. ResultsOur analyses showed that all health care service usage measures (e.g. access to emergency care; number of outpatient visits) and the total annual costs and pharmacy costs are associated significantly with comorbidity class. Instead, no differences in hospitalization rates by comorbidity class were revealed. ConclusionThe association between a larger number of comorbidities and higher total health care service usage and costs was seen mainly for primary care services. This underscores the need to strengthen primary care for today’s aging and multimorbid population.

Predicting the Cost of Health Care Services: A Comparison of Case-mix Systems and Comorbidity Indices That Use Administrative Data.

Case-mix systems and comorbidity indices aggregate clinical information about patients over time and are used to characterize need for health care services. These tools were validated for their original purpose, but those purposes are varied, and they have not been compared directly in the context of predicting costs of health care services. To compare predictions of next-year health care service costs across 4 tools, including: the Johns Hopkins Adjusted Clinical Groups (ACG), the Elixhauser Comorbidity Index, Charlson-Deyo Comorbidity Index, and the Canadian Institute for Health Information (CIHI) population grouper. British Columbia administrative data from fiscal years 2012-2013 were used to generate case-mix variables and the comorbidity indices. Outcome variables include next-year (2013-2014) total, physician, acute care, and pharmaceutical costs, Outcomes were modeled using 2-part models. Performance was compared using adjusted R, root mean squared error, and mean absolute error using the predicted and the actual next-year cost. Models including the CIHI grouper (239 conditions) and ACG system had similar performance in most cost categories and slightly better fit than Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). Adding a dummy variable for nonusers in the models for CCI and ECI increased R values slightly. All these systems have empirical support for use in predicting health care costs, despite in some cases being developed for other purposes. No system is particularly effective at predicting next-year acute care cost, likely because acute events are often by definition unexpected. The freely available ECI and CCI comorbidity indices implemented using the highest-performing methods developed here may be a good choice in many circumstances.

Impactibility Model for Population Health Management in High-Cost Elderly Heart Failure Patients: A Capture Method Using the ACG System.

The aim of the present study is to use the ACG (Adjusted Clinical Groups) System to create an impactibility model by identifying homogeneous clinical subgroups of patients with high risk of an adverse health outcome in a population of heart failure patients with complex health care needs (PCHCN). This method will allow policy makers to target and prioritize services for the highest risk PCHCN in the context of limited health care resources, by identifying relatively homogeneous groups of patients with similar comorbidities. Subjects classified in 2012 as PCHCN in a local health unit by the ACG System were linked with hospital discharge records in 2013. The authors applied the Apriori algorithm to identify the most common sets of the most predictive diseases for the following outcomes of interest: at least 1 admission and at least 1 preventable admission in the year. Predictive performance for the former outcome was compared between the impactability model with the available ACG's individual risk score. The Apriori algorithm also was applied to predict the latter outcome as an example of an event that a policy maker would be able to prevent. Evidence showed no statistically significant difference between the 2 methods. The present model also displayed evidence of good calibration. The Apriori algorithm was applied as an impactibility model, built based on the ACG System, that allowed the authors to obtain an "ACG-based group risk score" and use it to identify clinically homogeneous subgroups of PCHCN. This will help policy makers develop "tool kits" for homogeneous groups of patients that improve health outcomes.

Assessment of patient's and health professional's experience of integrated care: preliminary results from a pilot survey in Veneto Region-Italy

Assessment of patient's and health professional's experience of integrated care: preliminary results from a pilot survey in Veneto Region-Italy

Patterns of health care-related multimorbidity among breast cancer survivors.

115 Background: Breast cancer survivors may experience multiple health conditions, both pre-existing and related to the cancer and its treatment. The objective of this study is to evaluate the total burden of healthcare-related morbidity and multimorbidity among breast cancer survivors, in the first five years from diagnosis. Methods: A population-based cohort of 6,944 female five-year breast cancer survivors, diagnosed at 18 years or older from 2000-2003, was identified from the Cancer Registry of the province of British Columbia, Canada, and followed to end 2008. Subjects were linked to all their provincial healthcare records of hospitalizations and outpatient physician service claims, excluding oncology and emergency visits, from diagnosis to 5 years post-diagnosis. International Classification of Disease (ICD) diagnostic codes for “reason for service” were extracted, then assigned to one of 32 diagnosis groups (Aggregated Diagnosis Groups (ADGS)) of the Johns Hopkins Adjusted Clinical Group (ACG) System. The ACG system aggregates diagnoses with similar expected demand for healthcare, primarily based on level of severity and persistence of disease. ADG distributions were generated. Results: Only 380(5.5%) of the survivors were < 40 years old at diagnosis; 1670(24.1%) were > 70 years. The majority (5648(81.3%)) were Stage I or II. In the 5 year period from diagnosis, most patients had at least one type of morbidity, with 47% having 11-15 different types of morbidities. Over 90% of survivors had reports of major ( = very high expected healthcare resource use) signs or symptoms, 3258 (46.9%) survivors experienced major adverse effects/injuries, 3232 (46.5%) had unstable chronic medical conditions, and 2696 (38.8%) had major time-limited infections. Approximately 49.3% had stable or unstable psychosocial conditions. Conclusions: Breast cancer patients experience extensive multimorbidity within five years of diagnosis, with high expected healthcare utilization. These results highlight the importance of comprehensive multimorbidity assessment, with care recommendations, included in cancer survivorship care plans.

Los adjusted clinicals groups: un sistema de clasificación de pacientes por ajuste de riesgos

Adjusted Clinical Groups (ACG) are risk adjustment systems that classify people according to their age, sex and medical conditions, these people have a similar consumption of health resources over a given period of time. They were developed by Starfield and Weiner (Johns Hopkins University, USA, 1991), and their objective is to measure the degree of illness among patients based on the levels of comorbidity. Their main applications are: a) the evaluation of the efficiency in the use of health services, b) measurement of population's comorbidity, and c) to program the funding or capitation fee for groups of providers. Some of the advantages of the ACG system are the limited number of variables needed for each patient (age, sex and diagnosis) and the unit of measurement, which is the patient. Some characteristics of this population risk adjustment system are commented.

Adjusted Clinical Groups as a risk assessment model for healthcare resource allocation

There is increasing interest in the study and improvement of health system resource allocation. The Adjusted Clinical Groups (ACG) System measures the morbidity burden of patient populations based on disease patterns, age and gender. This system can be used in primary health care settings. To report the use of the ACG system as a risk based patient classification system and its potential as a resource allocation mechanism in primary health care. Patient diagnoses from a sample of primary care establishments were processed using the Johns Hopkins ACG System. Linear regressions were used to measure the ACG System's ability to explain resource use and ambulatory visit rates. The ACG System had a statistically significant capacity to explain primary health care costs with an adjusted R² of 0.26. These results are significantly better, compared with risk assessment models based on patients' age and sex, whose adjusted R² is 0.05. The use of risk adjustment mechanisms will contribute to improve health care resource allocation as well to know the disease profile of the population.

Quantifying morbidity burdens and medical utilization of children with intellectual disabilities in Taiwan: A nationwide study using the ACG case-mix adjustment system

The purpose of this study was to quantify morbidity burdens of children with intellectual disability (ID) and to examine its association with total medical utilization and expenditure on a national basis in Taiwan. People under 18 years of age that had been continuously enrolled in the National Health Insurance (NHI) between year 2008 and 2010 were selected from one million randomly-sampled NHI beneficiaries. The Johns Hopkins Adjusted Clinical Group (ACG) System was applied to evaluate an individual's morbidity burden using 2008-2010 claims data, including age, sex, diagnosis, pharmacy, ambulatory, and inpatient utilization and expenditure (in New Taiwan Dollars, NTDs). The ID prevalence rate was 0.69% for people aged under 18. People with ID could be assigned to 20 mutually exclusive ACGs and to five simplified morbidity categories: healthy (0.1%), low (1.5%), moderate (31.9%), high (44.0%), and very high (22.4%). People with ID had more per capita visits (108.4 vs. 51.5, p<0.001), hospital admission (27.7% vs. 13.1%, p<0.001), pharmacy (NTD 21,069 vs. 4983, p<0.001) and total expenditure (NTD 144,962 vs. 29,764, p<0.001) than those without ID over 3 years. Those who assigned to the high-morbid categories cost more in ambulatory and inpatient services than those with low to moderate morbidities. In conclusion, the morbidity burdens of people with ID can be quantified by the ACG System based on readily available data. Regularly evaluating morbidity burdens and medical utilization has particular relevance for planning high-quality and efficient care. People's disabilities and comorbid illnesses shall be treated by integrated multidisciplinary teams.

Measures of Multimorbidity and Morbidity Burden for Use in Primary Care and Community Settings: A Systematic Review and Guide

Many patients consulting in primary care have multiple conditions (multimorbidity). Aims of this review were to identify measures of multimorbidity and morbidity burden suitable for use in research in primary care and community populations, and to investigate their validity in relation to anticipated associations with patient characteristics, process measures, and health outcomes. Studies were identified using searches in MEDLINE and EMBASE from inception to December 2009 and bibliographies. Included were 194 articles describing 17 different measures. Commonly used measures included disease counts (n = 98), Chronic Disease Score (CDS)/RxRisk (n = 17), Adjusted Clinical Groups (ACG) System (n = 25), the Charlson index (n = 38), the Cumulative Index Illness Rating Scale (CIRS; n = 10) and the Duke Severity of Illness Checklist (DUSOI; n = 6). Studies that compared measures suggest their predictive validity for the same outcome differs only slightly. Evidence is strongest for the ACG System, Charlson index, or disease counts in relation to care utilization; for the ACG System in relation to costs; for Charlson index in relation to mortality; and for disease counts or Charlson index in relation to quality of life. Simple counts of diseases or medications perform almost as well as complex measures in predicting most outcomes. Combining measures can improve validity. The measures most commonly used in primary care and community settings are disease counts, Charlson index, ACG System, CIRS, CDS, and DUSOI. Different measures are most appropriate according to the outcome of interest. Choice of measure will also depend on the type of data available. More research is needed to directly compare performance of different measures.

Adaptive capacity of the Adjusted Clinical Groups Case-Mix System to the cost of primary healthcare in Catalonia (Spain): a observational study

ObjectivesTo describe the adaptive capacity of the Adjusted Clinical Groups (ACG) system to the cost of care in primary healthcare centres in Catalonia (Spain).DesignRetrospective study (multicentres) conducted using computerised medical...

Explaining Prescription Drug Use and Expenditures Using the Adjusted Clinical Groups Case-Mix System in the Population of British Columbia, Canada

Given that prescription drugs have become a major financial component of health care, there is an increased need to explain variations in the use of and expenditure on medicines. Case-mix systems built from existing administrative datasets may prove very useful for such prediction. We estimated the concurrent and prospective predictive validity of the adjusted clinical groups (ACG) system in pharmaceutical research and compared the ACG system with the Charlson index of comorbidity. We ran a generalized linear models to examine the predictive validity of the ACG system and the Charlson index and report the correlation between the predicted and observed expenditures. We reported mean predictive ratios across medical condition and cost-defined groups. When predicting use of medicines, we used C-statistics to summarize the area under the receiver operating characteristic curve. The 3,908,533 British Columbia residents who were registered for the universal health care plan for 275+ days in the calendar years 2004 and 2005. Outcomes were total pharmaceutical expenditures, use of any medicines, and use of medicines from 4+ different therapeutic categories. The ACG case mix system predicted drug expenditures better than the Charlson index. The mean predictive ratios for the ACG system models were all within 4% of the actual costs when examining medical condition group and the C-stats for the 2 dichotomous outcomes were between 0.82 and 0.89. ACG case-mix adjusters are a valuable predictor of pharmaceutical use and expenditures with much higher predictive power than age, sex, and the Charlson index of comorbidity.

An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan

BackgroundDiagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme.MethodsA random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level.ResultsThe more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall.ConclusionsGiven the widespread availability of claims data and the superior explanatory power of claims-based risk adjustment models over demographics-only models, Taiwan's government should consider using claims-based models for policy-relevant applications. The performance of the ACG case-mix system in Taiwan was comparable to that found in other countries. This suggested that the ACG system could be applied to Taiwan's NHI even though it was originally developed in the USA. Many of the findings in this paper are likely to be relevant to other diagnosis-based risk adjustment methodologies.

Utilización de los grupos clínicos ajustados en un centro de atención primaria español: estudio retrospectivo de base poblacional

To evaluate the results of implementing a classification system based on adjusted clinical groups (ACG) at a primary health care (PHC) in a Spanish population. A retrospective, cross-sectional study based on the computerized medical records of outpatients seen in 2007 by the La Roca clinic, administered by a health services management company in La Roca del Vallès, Barcelona, Spain. The ACGs were formed according to the International Classification of Diseases, 9th Revision, Clinical Modification. The relative weight of each ACG's total average cost was calculated (in U.S. dollars) and based on these, the resources usage levels were established. The risk index (RI) and efficiency index (EI) for 2006 and the classification's explanatory power were determined. A total of 8 294 patients were studied (82.7% coverage), with an average of 4.1 incidents per patient, 6.9 visits per patient, and 5.7 visits per person per year. Seven GCAs accounted for 51.0% of patients seen. The RI was 1.015, the EI was 0.975 visits, and the explanatory power of the ACG classification was 53.4% for visits and 74.8% for incidents. The ACG system allowed this patient population to be grouped by clinical status, which can help to, among other things, allocate resources and evaluate PHC team efficiency.

Quantifying morbidities by Adjusted Clinical Group system for a Taiwan population: A nationwide analysis

BackgroundThe Adjusted Clinical Group (ACG) system has been used in measuring an individual's and a population's morbidities. Although all required inputs for running the ACG system are readily available, patients' morbidities and their associations to health care utilizations have been rarely studied in Taiwan. Therefore, the objective of this study was using the ACG system to quantify morbidities for Taiwanese population and to examine their relationship to ambulatory utilizations and costs.MethodsThis secondary analysis examined claims data for ambulatory services provided to 2.71 million representative Taiwanese in 2002 and 2003. People were grouped by the ACG system according to age, gender, and all ambulatory diagnosis codes in a given year. The software collapses the full set of ACGs into six morbidity categories (Non-users, Healthy, Low-morbidity, Moderate-, High- and Very-high) termed Resource Utilization Bands (RUBs). Each ACG was assigned a relative weight (RW), which was calculated as the ratio of mean ambulatory cost for each ACG to that for the overall. The distribution of morbidities was compared between years 2002 and 2003. The consistency of the distributions of visits, costs, and RWs of each ACG were examined for a two-year period. The relationship between people's morbidities and their ambulatory utilizations and costs was assessed.ResultsNinety-eight percent of the subjects were correctly assigned to ACGs. Except for non-users (7.9 ~ 8.3%), most subjects were assigned to ACGs of acute and minor diseases and ACGs of moderate-to-high-morbid chronic diseases. The distributions of ACG-based morbidities were highly consistent (r = 0.949, p < 0.001) between 2002 and 2003. The ACG-specific visits (r = 0.955, p < 0.001), costs (r = 0.966, p < 0.001) and RWs (r = 0.991, p < 0.001) were correlated across two years. People grouped to the high-morbid ACGs had more visits and costs than those grouped to the low-morbid ACGs. Forty-six percent of the total ambulatory costs were spent by eighteen percent of the population, who were grouped to the High- and Very-high-morbidity RUBs.ConclusionThis study demonstrated the feasibility, validity, and reliability of using the ACG system to measure morbidities in a Taiwan population and to explain their associations with ambulatory utilizations and costs for the whole country.

Explanatory Ability of the ACG System Regarding the Utilization and Expenditure of the National Health Insurance Population in Taiwan—A 5-year Analysis

The adjusted clinical group (ACG) is a diagnosis-based case-mix adjustment system, which has been widely evaluated in several countries other than Taiwan. The aim of this study was to assess the performance of the ACG system on the National Health Insurance (NHI) population in Taiwan. We conducted longitudinal data analysis using the claims data of 1% of randomly sampled NHI enrollees from 2000 to 2004. The ACG software was used to assign each individual to 1 ACG category based on age, gender and aggregating diagnoses in each year from 2000 to 2004, respectively. The ACG distribution patterns and their relationships to expenditure were examined. Explanatory ability as measured by adjusted R2 of the ACG system for same-year and next-year ambulatory and inpatient expenditure were examined by multivariate regression models for each year. The quality of NHI claim data was satisfactory in that 98.1% of the population could be assigned to ACG categories. The population's ACG patterns were substantially consistent but unequally distributed across the 5 years. Eighty percent of NHI expenditure were spent on people assigned to 21 ACGs. The explanatory abilities of individual's ACG and its components with respect to the variance of same-year and next-year 99% truncated visits, ambulatory expenditure, inpatient expenditure, and total NHI expenditure were quite consistent across years and were superior to age and gender. The explanatory performance was better for ambulatory than inpatient expenditure and was comparable to the statistics demonstrated in other countries. The ACG system worked well for Taiwanese ambulatory visits and expenditure across years. Health care authorities can introduce the ACG system to quantify the population's morbidity burdens and medical needs.

Clinical categories of patients and encounter rates in primary health care – a three-year study in defined populations

BackgroundThe objective was to estimate the proportion of inhabitants with a diagnosis-registered encounter with a general practitioner, and to elucidate annual variations of clinical categories of patients in terms of their individual comorbidity.MethodsA three-year retrospective study of encounter data from electronic patient records, with an annual-based application of the Johns Hopkins Adjusted Clinical Groups (ACG) system. Data were retrieved from every patient with a diagnosis-registered encounter with a GP during the period 2001–2003 at 13 publicly managed primary health care centres in Blekinge county, southeastern Sweden, with about 150000 inhabitants. Main outcome measures: Proportions of inhabitants with a diagnosis-registered encounter, and ranges of the annual proportions of categories of patients according to ACGs.ResultsThe proportion of inhabitants with a diagnosis-registered encounter ranged from about 64.0% to 90.6% for the primary health care centres, and averaged about 76.5% for all inhabitants. In a three-year perspective the average range of categories of patients was about 0.4% on the county level, and about 0.9% on the primary health care centre level. About one third of the patients each year had a constellation of two or more types of morbidity.ConclusionAbout three fourths of all inhabitants had one or more diagnosis-registered encounters with a general practitioner during the three-year period. The annual variation of categories of patients according to ACGs was small on both the county and the primary health care centre level. The ACG system seems useful for demonstrating and predicting various aspects of clinical categories of patients in Swedish primary health care.

Adjusted Clinical Groups (ACGs) explain the utilization of primary care in Spain based on information registered in the medical records: A cross-sectional study

Background Previous studies have shown the validity of Adjusted Clinical Groups (ACGs) in the primary care setting, but the absence of administrative databases is a problem for the implementation of this system. Objectives To assess the validity of physicians’ notes in computerized clinical records for the implementation of ACGs, and to determine the ability of the ACGs system to explain the use of primary care resources in real conditions of the daily practice. Research design Cross-sectional study. Subjects All patients who were continuously assigned to 56 physicians from public primary health care centers in the Bizkaia Basque Country (Spain) over at least a 6-month period were included. Measures Patients were classified by means of the ACGs system using the ICD-9-CM diagnostic codes according to three scenarios: (1) those annotated by the physicians in the computerized medical record, (2) codification of computerized medical records diagnoses by the research team, and (3) computerized medical records diagnoses complemented by hospital discharge codes. Results The ACGs system explained more than 50% of the variance in visits made to primary care physicians, 25–40% of prescriptions, 25–30% of referrals and requests of laboratory tests, and 14–16% of radiographs. The coefficients of determination remained almost invariable after the addition of hospital diagnoses or correction of coding errors. Fifty-two physicians (93%) registered their patients’ diagnosis quite acceptably, whereas only 29 (52%) included this datum in the prescriptions. Conclusions The ACGs system is a useful tool to explain the use of primary care services in a national health care system within the European Union. The implementation of ACGs from the physician's annotations in the computerized medical records is feasible.