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Preventable hospitalizations and access to health care
HEALTH
C.IRII
cians report that patients treated m hralth care systems struttured diEerently from the non-VA hospital system in the United States wait significantly longer for cardiac cathetenzatlon and coronary arterty bypass surger)Artificial Neural Network for Diagnosis of Acute Pulmonary Embolism: Effect of Case and Observer Selection G.D. Tourasri, C.E. Floyd, H.D. Sortman, RE. Coleman. Department of Radiology, Duke University Medical Center, Durham, North Carolina Radidogy 1995;194889-93.
Purpose: To compare the diagnostic performance of an artificial neural network (ANN) with that of physicians m patients w-lth suspected pulmonary embolism (PE). Matcti& and M&&X An ANN was developed to predict PE by using findings from ventilation-perfusion lung scans and chest radiographs. First. the network was evaluated on 1.Oh4 cases from the Prospectlvc Investigation of Pulmonary Embohsm Dlagnosls (PlOPED study that had a definitive angiographlc outcome. An uppet and lower bound of its diagnostic performance was provided depending on case difficulty Then. the network was tested on 104 patients with suspected PE in whom pulmonary anglography was essential for diagnosis The diagnostic performance L~I the ANN was compared with that of (a) two nuclear medicint physicians who read the scans for the needs of this study and (b) the nuclear medicine physicians who ongmally read lhc, scans. The effects of case and obsener selection on perfotmanic, were addressed. Results: The ANN outperformed the physlclanh when they used the PIOPED criteria lor categoric assessment, and it performed as well as the two study physicians on the basis of their probability assessments. Conclusion. The ANN call detect or exclude PE m a highly selected group of difficult cast\ ulth a consistency equivalent to that of ivery experienced $1) sicians. Preventable Hospitalizations Health Care
and Access to
Andrew 8. Bindman, Kevin Grumbach, Denmr Osmond. Miriam Komaromy, Karen Vtanizan, Nicole Lurie, John Billings, Anita Stewart. Primary Care Research Center and Division of General Internal Medicine, San Francisco General Hospital; Departments of Medicine, Epidemiology and Biostatistics, and Family and Community Medicine, Institute for Health Policy Studies, Division of General Internal Medicine, and Institute for Health and Aging, University of California, San Francisco; Department of Medicine, Hennepin County Medical Center, University of Minnesota Schools of Medicine and Public Health, Minneapolis; and Robert F. Wagner Graduate khool of Public Service, New York University, New York, New Yolk JAHA 1995;274: 305-l I.
DELIVERY
Objccl~vr: To examine whether the higher hospital admission rates for chronic medical conditions such as asthma, hypertension, congestive heart failure, chronic obstructive pulmonary disease, and diabetes in low-income communities resulted from community differences in access to care. prevalence of the diseases, propensity to seek care, or physician admitting style. Design: Analysis of Callfornla hospital discharge data. We calculated the hospltallzatlon rates for these five chronic conditions for the 250 ZIP code clusters that define urban California. We performed a random-digit telephone survey among adults Iresiding In a random sample of 41 of these urban ZIP code clusters stratified by admission rates and a mailed survey of generalist and emergency physicians who practiced in the same 41 areas. Settmg: Community based. Particitxmts: A total of 6674 English- and Spanish-speaking adults aged 18 through 64 years residing in the 41 areas were asked about their access to care, their chronic medical conditions. and their propensity to seek health care. Physician admitting style was measured with written clinical vlgnrttes among 723 generalist and emergency physicians practicing in the same communities. Main OutLO~IL’ Mr~surc~ We compared respondents’ reports of access to medical care m an area with the area’s cumulative admlssion rate for these five chronic conditions. We then tested whether access to medical care remained independently associated with preventable hospitalization rates after controlling for the prevalence of the conditions, health care seekmg, and physician practice style. Results: Access to care was inversely associated myth the hospitalization rates for the five chronic medical conditions (R’=O.50; P<.OOl). In a mulu\~ariate analysis that included a measure of access, the prevalence of conditions, health care seeking, and phySKXII~ practice style to predict cumulative hospitalization rates tor chrome medical conditions, both self-rated access to care (FW.002) and the prevalence of the conditions i P<.O?) remamed independent predictors. Conclusion: Comrnunmes where people perceive poor access to medical care have higher rates of hospitalization for chronic diseases. Improving access to care 1s more likely than changing patients’ propensity to seek health care or eliminating variation in phy~ICIX~practice style to reduce hospitalization rates for chronic conditlonz
C.IRII
cians report that patients treated m hralth care systems struttured diEerently from the non-VA hospital system in the United States wait significantly longer for cardiac cathetenzatlon and coronary arterty bypass surger)Artificial Neural Network for Diagnosis of Acute Pulmonary Embolism: Effect of Case and Observer Selection G.D. Tourasri, C.E. Floyd, H.D. Sortman, RE. Coleman. Department of Radiology, Duke University Medical Center, Durham, North Carolina Radidogy 1995;194889-93.
Purpose: To compare the diagnostic performance of an artificial neural network (ANN) with that of physicians m patients w-lth suspected pulmonary embolism (PE). Matcti& and M&&X An ANN was developed to predict PE by using findings from ventilation-perfusion lung scans and chest radiographs. First. the network was evaluated on 1.Oh4 cases from the Prospectlvc Investigation of Pulmonary Embohsm Dlagnosls (PlOPED study that had a definitive angiographlc outcome. An uppet and lower bound of its diagnostic performance was provided depending on case difficulty Then. the network was tested on 104 patients with suspected PE in whom pulmonary anglography was essential for diagnosis The diagnostic performance L~I the ANN was compared with that of (a) two nuclear medicint physicians who read the scans for the needs of this study and (b) the nuclear medicine physicians who ongmally read lhc, scans. The effects of case and obsener selection on perfotmanic, were addressed. Results: The ANN outperformed the physlclanh when they used the PIOPED criteria lor categoric assessment, and it performed as well as the two study physicians on the basis of their probability assessments. Conclusion. The ANN call detect or exclude PE m a highly selected group of difficult cast\ ulth a consistency equivalent to that of ivery experienced $1) sicians. Preventable Hospitalizations Health Care
and Access to
Andrew 8. Bindman, Kevin Grumbach, Denmr Osmond. Miriam Komaromy, Karen Vtanizan, Nicole Lurie, John Billings, Anita Stewart. Primary Care Research Center and Division of General Internal Medicine, San Francisco General Hospital; Departments of Medicine, Epidemiology and Biostatistics, and Family and Community Medicine, Institute for Health Policy Studies, Division of General Internal Medicine, and Institute for Health and Aging, University of California, San Francisco; Department of Medicine, Hennepin County Medical Center, University of Minnesota Schools of Medicine and Public Health, Minneapolis; and Robert F. Wagner Graduate khool of Public Service, New York University, New York, New Yolk JAHA 1995;274: 305-l I.
DELIVERY
Objccl~vr: To examine whether the higher hospital admission rates for chronic medical conditions such as asthma, hypertension, congestive heart failure, chronic obstructive pulmonary disease, and diabetes in low-income communities resulted from community differences in access to care. prevalence of the diseases, propensity to seek care, or physician admitting style. Design: Analysis of Callfornla hospital discharge data. We calculated the hospltallzatlon rates for these five chronic conditions for the 250 ZIP code clusters that define urban California. We performed a random-digit telephone survey among adults Iresiding In a random sample of 41 of these urban ZIP code clusters stratified by admission rates and a mailed survey of generalist and emergency physicians who practiced in the same 41 areas. Settmg: Community based. Particitxmts: A total of 6674 English- and Spanish-speaking adults aged 18 through 64 years residing in the 41 areas were asked about their access to care, their chronic medical conditions. and their propensity to seek health care. Physician admitting style was measured with written clinical vlgnrttes among 723 generalist and emergency physicians practicing in the same communities. Main OutLO~IL’ Mr~surc~ We compared respondents’ reports of access to medical care m an area with the area’s cumulative admlssion rate for these five chronic conditions. We then tested whether access to medical care remained independently associated with preventable hospitalization rates after controlling for the prevalence of the conditions, health care seekmg, and physician practice style. Results: Access to care was inversely associated myth the hospitalization rates for the five chronic medical conditions (R’=O.50; P<.OOl). In a mulu\~ariate analysis that included a measure of access, the prevalence of conditions, health care seeking, and phySKXII~ practice style to predict cumulative hospitalization rates tor chrome medical conditions, both self-rated access to care (FW.002) and the prevalence of the conditions i P<.O?) remamed independent predictors. Conclusion: Comrnunmes where people perceive poor access to medical care have higher rates of hospitalization for chronic diseases. Improving access to care 1s more likely than changing patients’ propensity to seek health care or eliminating variation in phy~ICIX~practice style to reduce hospitalization rates for chronic conditlonz