Programme no. 540-OP
Measuring Care Coordination to Identify Patients at Risk
K Kinder*1, C Pollack2, K. Lemke3
1Bloomberg School of Public Health,Johns Hopkins University,Baltimore,United States, 2Bloomberg School of Public Health,Johns Hopkins University,Baltimore,United States, 3Bloomberg School of Public Health,Johns Hopkins University,Baltimore,United States
* = Presenting author
Objectives: This presentation will address the impact of information on improving coordination of care across the spectrum of the health care system, specifically presenting the existing scientific evidence regarding:
- the differentiation between primary and specialty care, and issues surrounding coordination,
- the impact of multi-morbidity on the delivery of care, including the identification of patients at risk of poor coordination,
- strategies to implement more appropriate information interfaces between clinicians.
Background: Coordination of care is threatened when information does not readily flow between those involved in delivering care. 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.
Results: A recent study of the Care Density measure on 9,596 patients with congestive heart failure (CHF) and 52,688 with diabetes 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.
Material/Methods: 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. The coordination markers use the clinician taxonomy to determine if a clinician can manage and coordinate the medical needs of the patient. 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.
With the upcoming new release of the ACG System, “Care Density”, a measure of patient sharing among physicians, will be introduced. 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.
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.
Points for discussion: