Karen Kinder

Multimorbidity and its measurement

Multimorbidity is increasing in frequency. It can be quantitatively measured and is a major correlate of high use of health services resources of all types, especially over time. The ACG System for characterizing multimorbidity is the only widely used method that is based on combinations of different TYPES of diagnoses over time, rather than the presence or absence of particular conditions or numbers of conditions. It incorporates administrative data (as from claims forms or medical records) on all types of encounters and is not limited to diagnoses captured during hospitalizations or other places of encounter. It can be employed in any one or combination of analytic models, and can incorporate medication use if desired. It is being used in clinical care, management of health services resources, in health services research to control for degree of morbidity, and in understanding morbidity patterns over time. In addition to its research uses, it is being employed in many countries in various applications as a policy to better understand health needs of populations and tailor health services resources to health needs.

Determining the morbidity profile, health-service utilization and health-provider efficiency in the Rejang River Basin, Sarawak, based on TPC® data and the Johns Hopkins ACG® System

The study focused on the application of case-mix in remote regions of the Rejang River basin of the state of Sarawak, Malaysia, to improve the delivery of health care for these less-accessible populations. The aim of this project was to determine the benefits of applying the ACG® System on available Teleprimary Care® (TPC) data from multiple primary health-care providers in remote regions of Sarawak, Malaysia, to gain a better understanding of the differences in morbidity burden and resource need by various population sub-groups, as compared to those in less-remote settings. It also focused on how the ACG® System could assist to identify “high-risk” patients, allowing for more targeted interventions, and highlighted how the results established a fairer basis upon which to assess provider performance. In addition, it facilitated profiling on a regional basis to improve efficiency in the delivery of primary health care in these remote regions.

Assessing socioeconomic health care utilization inequity in Israel: impact of alternative approaches to morbidity adjustment

BackgroundThe ability to accurately detect differential resource use between persons of different socioeconomic status relies on the accuracy of health-needs adjustment measures. This study tests different approaches to morbidity adjustment in explanation of health care utilization inequity.MethodsA representative sample was selected of 10 percent (~270,000) adult enrolees of Clalit Health Services, Israels largest health care organization. The Johns-Hopkins University Adjusted Clinical Groups® were used to assess each persons overall morbidity burden based on one years (2009) diagnostic information. The odds of above average health care resource use (primary care visits, specialty visits, diagnostic tests, or hospitalizations) were tested using multivariate logistic regression models, separately adjusting for levels of health-need using data on age and gender, comorbidity (using the Charlson Comorbidity Index), or morbidity burden (using the Adjusted Clinical Groups). Model fit was assessed using tests of the Area Under the Receiver Operating Characteristics Curve and the Akaike Information Criteria.ResultsLow socioeconomic status was associated with higher morbidity burden (1.5-fold difference). Adjusting for health needs using age and gender or the Charlson index, persons of low socioeconomic status had greater odds of above average resource use for all types of services examined (primary care and specialist visits, diagnostic tests, or hospitalizations). In contrast, after adjustment for overall morbidity burden (using Adjusted Clinical Groups), low socioeconomic status was no longer associated with greater odds of specialty care or diagnostic tests (OR: 0.95, CI: 0.94-0.99; and OR: 0.91, CI: 0.86-0.96, for specialty visits and diagnostic respectively). Tests of model fit showed that adjustment using the comprehensive morbidity burden measure provided a better fit than age and gender or the Charlson Index.ConclusionsIdentification of socioeconomic differences in health care utilization is an important step in disparity reduction efforts. Adjustment for health-needs using a comprehensive morbidity burden diagnoses-based measure, this study showed relative underutilization in use of specialist and diagnostic services, and thus allowed for identification of inequity in health resources use, which could not be detected with less comprehensive forms of health-needs adjustments.

The application of ACG predictive models to the English population for the purposes of funding allocation

The current budgetary allocation in the UK to primary care trusts is based on capitation adjusted for demographic, socio-economic and population morbidity utilising aggregate and survey statistics. The ACG system has been used to stratify populations according to risk and investigate adjusting budgets using person-based morbidity data as an alternative. The ACG predictive models utilised hospital secondary care data, GP primary care data, and pharmacy and individual resident population data up to 50 million individuals.

Primary care priorities in addressing health equity: summary of the WONCA 2013 health equity workshop

BackgroundResearch consistently shows that gaps in health and health care persist, and are even widening. While the strength of a country´s primary health care system and its primary care attributes significantly improves populations´ health and reduces inequity (differences in health and health care that are unfair and unjust), many areas, such as inequity reduction through the provision of health promotion and preventive services, are not explicitly addressed by general practice. Substantiating the role of primary care in reducing inequity as well as establishing educational training programs geared towards health inequity reduction and improvement of the health and health care of underserved populations are needed.MethodsThis paper summarizes the work performed at the World WONCA (World Organization of National Colleges and Academies of Family Medicine) 2013 Meetings´ Health Equity Workshop which aimed to explore how a better understanding of health inequities could enable primary care providers (PCPs)/general practitioners (GPs) to adopt strategies that could improve health outcomes through the delivery of primary health care. It explored the development of a health equity curriculum and opened a discussion on the future and potential impact of health equity training among GPs.ResultsA survey completed by workshop participants on the current and expected levels of primary care participation in various inequity reduction activities showed that promoting access (availability and coverage) to primary care services was the most important priority. Assessment of the gaps between current and preferred priorities showed that to bridge expectations and actual performance, the following should be the focus of governments and health care systems: forming cross-national collaborations; incorporating health equity and cultural competency training in medical education; and, engaging in initiation of advocacy programs that involve major stakeholders in equity promotion policy making as well as promoting research on health equity.ConclusionsThis workshop formed the basis for the establishment of WONCA´s Health Equity Special Interest Group, set up in early 2014, aiming to bring the essential experience, skills and perspective of interested GPs around the world to address differences in health that are unfair, unjust, unnecessary but avoidable.

Improving primary care through information. A Wonca keynote paper.

Abstract Information from health care encounters across the entire health care spectrum, when consistently collected, analysed and applied can provide a clearer picture of patients’ history as well as current and future needs through a better understanding of their morbidity burden and health care experiences. It can facilitate clinical activity to target limited resources to those patients most in need through risk adjustment mechanisms that consider the morbidity burden of populations, and it can help target quality improvement and cost saving activities in the right places. It can also open the door to a new chapter of evidence-based medicine around multi-morbidity. In summary, it can support a better integrated health system where primary care can provide continuous, coordinated, and comprehensive person-centred care to those who could benefit most. This paper explores the potential uses of information collected in electronic health records (EHRs) to inform case-mix and predictive modelling, as well as the associated challenges, with a particular focus on their application to primary care.

Care Coordination - How can we measure it?

Introduction : In the definition of primary care put forward by Dr. Barbara Starfield, primary care refers those health-related needs of people “too uncommon to maintain competence and coordinates care when people receive services at other levels of care” (1) Patients with poorly coordinated care are likely to have more costly and lower quality health care due to factors such as excess utilization resulting from redundant investigations, potentially harmful missed drug-disease interactions, and lower patient satisfaction. Therefore, the identification of patients at risk of poor coordination is essential. This presentation will address the impact of information on improving coordination of care across the spectrum of the health care and community services systems. As morbidity burden increases the number of different clinicians seen rises(2), yet coordination of care is threatened when information does not readily flow between those involved in delivering care. Case-mix tools which transform routinely collected electronic health data into actionable information can support both the clinicians decision making process and the policymaker to provide better coordinated care through the exchange of clinical data, measurement of patients’ needs, and a better understanding of the use of healthcare resources. Thus the imperative for coordination requires that all information generated in the care of patients be captured in the care provided over time. Theory / Method : The Johns Hopkins ACG® System has developed four complementary coordination markers as well as a coordination risk score to systematically assess the risk of poor coordination of care. In combination, the markers can identify populations at risk for poor coordination which has implications for cost, quality, and performance assessment. Greater insight about the convergence of risk, medical utilization and prescribing patterns can be captured by combining risk defined by diagnoses with risk defined by pharmacy information. Further, studies have shown that when clinicians share patients with other clinicians more frequently, they are more likely to have referral relationships and seek advice. With the release of Version 11, the ACG System introduced a measure of patient sharing among physicians, termed “Care Density”. This patient-level measure assesses the number of individual clinicians a patient sees and the degree to which those clinicians share other patients. The care density measure is based on the hypothesis that patients seen by clinicians who share patients more frequently have higher levels of communication and information sharing. (3,4) Results : An initial study of the Care Density measure on 9,596 patients with congestive heart failure (CHF) and 52,688 with diabetes (DM) demonstrated a significant correlation between lower inpatient costs and rates of hospitalization amongst those patients with high care density. Also, for diabetic patients with high care density, lower outpatient costs and higher pharmacy costs were found.(3) A more recent study of the Care Density measure expanded its population size to 1.7 million patients with CHF, COPD and/or Diabetes. Among all patients, patients with the highest care density—indicating high levels of patient-sharing among their office-based physicians— had significantly lower rates of adverse events measured as Prevention Quality Indicators (PQIs) compared to patients with low care density (Odds Ratio [OR] 0.88, 95% Confidence Interval [CI] 0.85-0.92). A significant association between care density and PQIs was also observed for patients with DM but not CHF or COPD.(4) Diabetic patients with higher care density scores had significantly lower odds of 30-day readmissions (OR 0.68, 95%CI 0.48-0.97). Significant associations were observed between care density and HEDIS measures though not always in the expected direction.(4) Discussion & Conclusion : Through a better understanding of how patients are shared amongst clinicians, as well as identifying those patients at risk of uncoordinated care, coordination can be improved, rates of hospitalization reduced and potential cost savings achieved. Further research is necessary to substantiate these results in other health care settings. References: 1- Starfield. Primary Care: Balancing Health Needs, Services, and Technology. Oxford U. Press. 1998. 2- Starfield et al, J Am Board Fam Pract 2002;15:473-80 3- Pollack, et.al, Care Density and Costs of Care, J Gen Intern Med 2013;28(3):459-65. 4- Pollack, et.al, Patient sharing and quality of care: measuring outcomes of care coordination using claims data. Med Care 2015;53:317-323.