Home Research Research Library Variation over time in preventable hospitalization rates across counties Variation over time in preventable hospitalization rates across counties 2011 Author(s) Sumner, W, and Hagen, Michael D Topic(s) Education & Training, Role of Primary Care, and Achieving Health System Goals Keyword(s) Clinical Simulation, and Quality Of Care Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine BACKGROUND: The Agency for Health Care Research and Quality developed 14 prevention quality indicators (PQIs), including four PQIs related to preventable hospitalizations for diabetes and one to asthma. Quality indicators vary across counties, but variation over time has not been described. METHODS: The Kentucky Cabinet for Health and Family Services published PQI data for each county in Kentucky in each of the 3 years from 2006 to 2008. Variation and correlations among PQI measures were calculated. RESULTS: PQI rates often varied 10-fold between counties. Repeated measures of four PQIs were highly correlated, suggesting local health care processes that are stable over time. Some PQIs, such as PQI01–emergent complications of blood glucose control–correlated poorly with other measures. Other PQIs are correlated over geography and time, including PQI03 (long-term complications of diabetes); PQI14 (poorly controlled diabetes); and PQI15 (asthma). CONCLUSIONS: These county PQI measures were stable over time. Stability implies that PQI measures were not the result of random processes and did not rapidly shift. However, some health improvement needs varied between counties. Although tailoring health promotion interventions to each county’s needs may be complex, stable needs afford time to undertake targeted quality improvement efforts. ABFM Research Read all 2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care 2025 Physician and Practice Characteristics Associated with Family Physician Panel Size Go to Physician and Practice Characteristics Associated with Family Physician Panel Size 2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning 2015 Fewer family physicians are in solo practices Go to Fewer family physicians are in solo practices
Author(s) Sumner, W, and Hagen, Michael D Topic(s) Education & Training, Role of Primary Care, and Achieving Health System Goals Keyword(s) Clinical Simulation, and Quality Of Care Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine
ABFM Research Read all 2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care 2025 Physician and Practice Characteristics Associated with Family Physician Panel Size Go to Physician and Practice Characteristics Associated with Family Physician Panel Size 2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning 2015 Fewer family physicians are in solo practices Go to Fewer family physicians are in solo practices
2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care
2025 Physician and Practice Characteristics Associated with Family Physician Panel Size Go to Physician and Practice Characteristics Associated with Family Physician Panel Size
2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning
2015 Fewer family physicians are in solo practices Go to Fewer family physicians are in solo practices