OBJECTIVES Analyses of emergency department (ED) use require visit classification algorithms base... more OBJECTIVES Analyses of emergency department (ED) use require visit classification algorithms based on administrative data. Our objectives were to present an expanded and revised version of an existing algorithm and to use this tool to characterize patterns of ED use across US hospitals and within a large sample of health plan enrollees. STUDY DESIGN Observational study using National Hospital Ambulatory Medical Care Survey ED public use files and hospital billing data for a health plan cohort. METHODS Our Johns Hopkins University (JHU) team classified many uncategorized diagnosis codes into existing New York University Emergency Department Algorithm (NYU-EDA) categories and added 3 severity levels to the injury category. We termed this new algorithm the NYU/JHU-EDA. We then compared visit distributions across these 2 algorithms and 2 other previous revised versions of the NYU-EDA using our 2 data sources. RESULTS Applying the newly developed NYU/JHU-EDA, we classified 99% of visits. Based on our analyses, it is evident that an even greater number of US ED visits than categorized by the NYU-EDA are nonemergent. For the first time, we provide a more complete picture of the level of severity among patients treated for injuries within US hospital EDs, with about 86% of such visits being nonsevere. Also, both the original and updated classification tools suggest that, of the 38% of ED visits that are clinically emergent, the majority either do not require ED resources or could have been avoided with better primary care. CONCLUSIONS The updated NYU/JHU-EDA taxonomy appears to offer cogent retrospective inferences about population-level ED utilization.
BACKGROUND Clinical information may frequently be missing from the electronic health record (EHR)... more BACKGROUND Clinical information may frequently be missing from the electronic health record (EHR), and contributes to delayed care, adverse events, and additional services, which may be costly. Missing laboratory data might be valuable marker for population-level risk stratification to help identify patients at risk of high cost and utilization. OBJECTIVE To determine whether absent hemoglobin HbA1c results in the EHR stratifies risk of high healthcare costs and utilization among adults with diabetes mellitus (DM). METHODS Retrospective U.S. cohort with EHR and claims data (2012-2013) of 6,270 continuously insured and care-engaged patients with DM who had ≥1 ambulatory visit in 2012. HbA1c availability defined as “HbA1c present” if ≥1 HbA1c EHR result was available in 2012 and otherwise as “HbA1c absent.” Patient’s annual healthcare costs, presence of any inpatient hospitalization, and presence of any emergency department (ED) visit in 2012 (concurrent) and 2013 (prospective).We use...
The Affordable Care Act calls for the establishment of statelevel health insurance exchanges. The... more The Affordable Care Act calls for the establishment of statelevel health insurance exchanges. The viability and success of these exchanges will require effective risk-adjustment strategies to compensate for differences in enrollees’ health status across health plans. This article describes why the Affordable Care Act could lead to favorable or adverse risk selection across plans. It reviews provisions in the act and recent proposed regulations intended to mitigate the problem of risk selection. We performed a simulation that showed that under the premium rating restrictions in the law, large incentives for insurers to attract healthier enrollees will be likely to persist—resulting in substantial overpayment to plans with very healthy enrollees and underpayment to plans with very sick members. We conclude that risk adjustment based on patients’ diagnoses, such as will be in place from 2014 on, will yield payments to insurers that will be more accurate than what will come solely from ...
In January 2020, the US declared the coronavirus outbreak a public health emergency and subsequen... more In January 2020, the US declared the coronavirus outbreak a public health emergency and subsequently the CDC issued guidelines for personal mitigation behavior, such as mask-wearing, hand-washing, and social-distancing. We examine individual socio-economic factors that potentially predict mitigation compliance using public data. We hypothesize that health risk factors, presence of symptoms, and psychological wellbeing predict mitigation behavior. Understanding what factors are associated with mitigation behavior will be important for policy makers in their efforts to curb the COVID-19 pandemic. The pandemic prompted strong mitigation behavior by adults, especially among females, non-whites, urban dwellers, and the psychological unwell. Other positive predictors were post-secondary education and higher income. Health symptoms and clinical risk factors did not predict increased mitigation practices, nor did age 65+ and proximity to infected persons. Our study findings are congruent wi...
Supplemental Digital Content is available in the text. Background: An individual’s risk for futur... more Supplemental Digital Content is available in the text. Background: An individual’s risk for future opioid overdoses is usually assessed using a 12-month “lookback” period. Given the potential urgency of acting rapidly, we compared the performance of alternative predictive models with risk information from the past 3, 6, 9, and 12 months. Methods: We included 1,014,033 Maryland residents aged 18–80 with at least 1 opioid prescription and no recorded death in 2015. We used 2015 Maryland prescription drug monitoring data to identify risk factors for nonfatal opioid overdoses from hospital discharge records and investigated fatal opioid overdose from medical examiner data in 2016. Prescription drug monitoring program–derived predictors included demographics, payment sources for opioid prescriptions, count of unique opioid prescribers and pharmacies, and quantity and types of opioids and benzodiazepines filled. We estimated a series of logistic regression models that included 3, 6, 9, and 12 months of prescription drug monitoring program data and compared model performance, using bootstrapped C-statistics and associated 95% confidence intervals. Results: For hospital-treated nonfatal overdose, the C-statistic increased from 0.73 for a model including only the fourth quarter to 0.77 for a model with 4 quarters of data. For fatal overdose, the area under the curve increased from 0.80 to 0.83 over the same models. The strongest predictors of overdose were prescription fills for buprenorphine and Medicaid and Medicare as sources of payment. Conclusions: Models predicting opioid overdose using 1 quarter of data were nearly as accurate as models using all 4 quarters. Models with a single quarter may be more timely and easier to identify persons at risk of an opioid overdose.
OBJECTIVES This exploratory study used outpatient laboratory test results from electronic health ... more OBJECTIVES This exploratory study used outpatient laboratory test results from electronic health records (EHRs) for patient risk assessment and evaluated whether risk markers based on laboratory results improve the performance of diagnosis- and pharmacy-based predictive models for healthcare outcomes. STUDY DESIGN Observational study of a patient cohort over 2 years. METHODS We used administrative claims and EHR data over a 2-year period for a population of continuously insured patients in an integrated health system who had at least 1 ambulatory visit during the first year. We performed regression tree analyses to develop risk markers from frequently ordered outpatient laboratory tests. We added these risk markers to demographic and Charlson Comorbidity Index models and 3 models from the Johns Hopkins Adjusted Clinical Groups system to predict individual cost, inpatient admission, and high-cost patients. We evaluated the predictive and discriminatory performance of 5 lab-enhanced m...
This exploratory study used outpatient laboratory test results from electronic health records (EH... more This exploratory study used outpatient laboratory test results from electronic health records (EHRs) for patient risk assessment and evaluated whether risk markers based on laboratory results improve the performance of diagnosis- and pharmacy-based predictive models for healthcare outcomes. Observational study of a patient cohort over 2 years. We used administrative claims and EHR data over a 2-year period for a population of continuously insured patients in an integrated health system who had at least 1 ambulatory visit during the first year. We performed regression tree analyses to develop risk markers from frequently ordered outpatient laboratory tests. We added these risk markers to demographic and Charlson Comorbidity Index models and 3 models from the Johns Hopkins Adjusted Clinical Groups system to predict individual cost, inpatient admission, and high-cost patients. We evaluated the predictive and discriminatory performance of 5 lab-enhanced models. Our study population incl...
To assess the effects of Hurricane Katrina on mortality, morbidity, disease prevalence, and servi... more To assess the effects of Hurricane Katrina on mortality, morbidity, disease prevalence, and service utilization during 1 year in a cohort of 20,612 older adults who were living in New Orleans, Louisiana, before the disaster and who were enrolled in a managed care organization (MCO). Observational study comparing mortality, morbidity, and service use for 1 year before and after Hurricane Katrina, augmented by a stratified random sample of 303 enrollees who participated in a telephone survey after Hurricane Katrina. Sources of data for health and service use were MCO claims. Mortality was based on reports to the MCO from the Centers for Medicare & Medicaid Services; morbidity was measured using adjusted clinical groups case-mix methods derived from diagnoses in ambulatory and hospital claims data. Mortality in the year following Hurricane Katrina was not significantly elevated (4.3% before vs 4.9% after the hurricane). However, overall morbidity increased by 12.6% (P <.001) compared with a 3.4% increase among a national sample of Medicare managed care enrollees. Nonwhite subjects from Orleans Parish experienced a morbidity increase of 15.9% (P <.001). The prevalence of numerous treated medical conditions increased, and emergency department visits and hospitalizations remained significantly elevated during the year. The enormous health burden experienced by older individuals and the disruptions in service utilization reveal the long-term effects of Hurricane Katrina on this vulnerable population. Although quick rebuilding of the provider network may have attenuated more severe health outcomes for this managed care population, new policies must be introduced to deal with the health consequences of a major disaster.
6026 Background: Many cancer survivors have comorbid conditions, adding complexity to their alrea... more 6026 Background: Many cancer survivors have comorbid conditions, adding complexity to their already complicated care and requiring greater care coordination. We assessed the role of care coordination in comorbid condition care for cancer survivors. Methods: Using SEER-Medicare, we examined 7 published indicators of quality comorbid condition care in survivors of loco-regional breast, prostate, or colorectal cancer who were diagnosed in 2004, in fee-for-service Medicare, and survived ≥3 years. Comorbid condition care was evaluated during the transition from initial cancer treatment to survivorship (i.e. days 366-1095 post-diagnosis). Coordination risk was categorized as Likely, Possible, or Unlikely using an index developed and tested as part of the ACG case-mix adjustment and predictive modeling tool. The index factors in the number of unique providers, number of specialties, the percentage majority source of care, and generalist visits. We tested the hypothesis that lower coordination risk would be associated with better comorbid condition care using logistic regression, adjusting for socio-demographics, cancer type, and comorbidity. Results: The sample included 8661 survivors (53% prostate, 22% breast, 26% colorectal; mean age 75; 65% male, 85% white). Our hypothesis was not supported. Compared to patients with Unlikely coordination issues, patients with Likely coordination issues were more likely to receive appropriate care on 4 indicators and less likely on 1. Possible coordination issues were associated with better care on 1 indicator and worse care on 1 indicator. To explore this finding further, we conducted post-hoc analyses examining the role of each component of the coordination risk index. Having more unique providers was generally associated with better comorbid condition care, in contrast to the calculation of the index which considers more unique providers a greater risk for coordination issues. Conclusions: These findings suggest that traditional metrics of care coordination may not be valid for survivors of cancer. Understanding the role of care coordination in cancer survivorship care requires development and application of alternative coordination measures.
IMPORTANCE This study assesses the role of telehealth in the delivery of care at the start of the... more IMPORTANCE This study assesses the role of telehealth in the delivery of care at the start of the COVID-19 pandemic. OBJECTIVES To document patterns and costs of ambulatory care in the US before and during the initial stage of the pandemic and to assess how patient, practitioner, community, and COVID-19-related factors are associated with telehealth adoption.
OBJECTIVES Analyses of emergency department (ED) use require visit classification algorithms base... more OBJECTIVES Analyses of emergency department (ED) use require visit classification algorithms based on administrative data. Our objectives were to present an expanded and revised version of an existing algorithm and to use this tool to characterize patterns of ED use across US hospitals and within a large sample of health plan enrollees. STUDY DESIGN Observational study using National Hospital Ambulatory Medical Care Survey ED public use files and hospital billing data for a health plan cohort. METHODS Our Johns Hopkins University (JHU) team classified many uncategorized diagnosis codes into existing New York University Emergency Department Algorithm (NYU-EDA) categories and added 3 severity levels to the injury category. We termed this new algorithm the NYU/JHU-EDA. We then compared visit distributions across these 2 algorithms and 2 other previous revised versions of the NYU-EDA using our 2 data sources. RESULTS Applying the newly developed NYU/JHU-EDA, we classified 99% of visits. Based on our analyses, it is evident that an even greater number of US ED visits than categorized by the NYU-EDA are nonemergent. For the first time, we provide a more complete picture of the level of severity among patients treated for injuries within US hospital EDs, with about 86% of such visits being nonsevere. Also, both the original and updated classification tools suggest that, of the 38% of ED visits that are clinically emergent, the majority either do not require ED resources or could have been avoided with better primary care. CONCLUSIONS The updated NYU/JHU-EDA taxonomy appears to offer cogent retrospective inferences about population-level ED utilization.
BACKGROUND Clinical information may frequently be missing from the electronic health record (EHR)... more BACKGROUND Clinical information may frequently be missing from the electronic health record (EHR), and contributes to delayed care, adverse events, and additional services, which may be costly. Missing laboratory data might be valuable marker for population-level risk stratification to help identify patients at risk of high cost and utilization. OBJECTIVE To determine whether absent hemoglobin HbA1c results in the EHR stratifies risk of high healthcare costs and utilization among adults with diabetes mellitus (DM). METHODS Retrospective U.S. cohort with EHR and claims data (2012-2013) of 6,270 continuously insured and care-engaged patients with DM who had ≥1 ambulatory visit in 2012. HbA1c availability defined as “HbA1c present” if ≥1 HbA1c EHR result was available in 2012 and otherwise as “HbA1c absent.” Patient’s annual healthcare costs, presence of any inpatient hospitalization, and presence of any emergency department (ED) visit in 2012 (concurrent) and 2013 (prospective).We use...
The Affordable Care Act calls for the establishment of statelevel health insurance exchanges. The... more The Affordable Care Act calls for the establishment of statelevel health insurance exchanges. The viability and success of these exchanges will require effective risk-adjustment strategies to compensate for differences in enrollees’ health status across health plans. This article describes why the Affordable Care Act could lead to favorable or adverse risk selection across plans. It reviews provisions in the act and recent proposed regulations intended to mitigate the problem of risk selection. We performed a simulation that showed that under the premium rating restrictions in the law, large incentives for insurers to attract healthier enrollees will be likely to persist—resulting in substantial overpayment to plans with very healthy enrollees and underpayment to plans with very sick members. We conclude that risk adjustment based on patients’ diagnoses, such as will be in place from 2014 on, will yield payments to insurers that will be more accurate than what will come solely from ...
In January 2020, the US declared the coronavirus outbreak a public health emergency and subsequen... more In January 2020, the US declared the coronavirus outbreak a public health emergency and subsequently the CDC issued guidelines for personal mitigation behavior, such as mask-wearing, hand-washing, and social-distancing. We examine individual socio-economic factors that potentially predict mitigation compliance using public data. We hypothesize that health risk factors, presence of symptoms, and psychological wellbeing predict mitigation behavior. Understanding what factors are associated with mitigation behavior will be important for policy makers in their efforts to curb the COVID-19 pandemic. The pandemic prompted strong mitigation behavior by adults, especially among females, non-whites, urban dwellers, and the psychological unwell. Other positive predictors were post-secondary education and higher income. Health symptoms and clinical risk factors did not predict increased mitigation practices, nor did age 65+ and proximity to infected persons. Our study findings are congruent wi...
Supplemental Digital Content is available in the text. Background: An individual’s risk for futur... more Supplemental Digital Content is available in the text. Background: An individual’s risk for future opioid overdoses is usually assessed using a 12-month “lookback” period. Given the potential urgency of acting rapidly, we compared the performance of alternative predictive models with risk information from the past 3, 6, 9, and 12 months. Methods: We included 1,014,033 Maryland residents aged 18–80 with at least 1 opioid prescription and no recorded death in 2015. We used 2015 Maryland prescription drug monitoring data to identify risk factors for nonfatal opioid overdoses from hospital discharge records and investigated fatal opioid overdose from medical examiner data in 2016. Prescription drug monitoring program–derived predictors included demographics, payment sources for opioid prescriptions, count of unique opioid prescribers and pharmacies, and quantity and types of opioids and benzodiazepines filled. We estimated a series of logistic regression models that included 3, 6, 9, and 12 months of prescription drug monitoring program data and compared model performance, using bootstrapped C-statistics and associated 95% confidence intervals. Results: For hospital-treated nonfatal overdose, the C-statistic increased from 0.73 for a model including only the fourth quarter to 0.77 for a model with 4 quarters of data. For fatal overdose, the area under the curve increased from 0.80 to 0.83 over the same models. The strongest predictors of overdose were prescription fills for buprenorphine and Medicaid and Medicare as sources of payment. Conclusions: Models predicting opioid overdose using 1 quarter of data were nearly as accurate as models using all 4 quarters. Models with a single quarter may be more timely and easier to identify persons at risk of an opioid overdose.
OBJECTIVES This exploratory study used outpatient laboratory test results from electronic health ... more OBJECTIVES This exploratory study used outpatient laboratory test results from electronic health records (EHRs) for patient risk assessment and evaluated whether risk markers based on laboratory results improve the performance of diagnosis- and pharmacy-based predictive models for healthcare outcomes. STUDY DESIGN Observational study of a patient cohort over 2 years. METHODS We used administrative claims and EHR data over a 2-year period for a population of continuously insured patients in an integrated health system who had at least 1 ambulatory visit during the first year. We performed regression tree analyses to develop risk markers from frequently ordered outpatient laboratory tests. We added these risk markers to demographic and Charlson Comorbidity Index models and 3 models from the Johns Hopkins Adjusted Clinical Groups system to predict individual cost, inpatient admission, and high-cost patients. We evaluated the predictive and discriminatory performance of 5 lab-enhanced m...
This exploratory study used outpatient laboratory test results from electronic health records (EH... more This exploratory study used outpatient laboratory test results from electronic health records (EHRs) for patient risk assessment and evaluated whether risk markers based on laboratory results improve the performance of diagnosis- and pharmacy-based predictive models for healthcare outcomes. Observational study of a patient cohort over 2 years. We used administrative claims and EHR data over a 2-year period for a population of continuously insured patients in an integrated health system who had at least 1 ambulatory visit during the first year. We performed regression tree analyses to develop risk markers from frequently ordered outpatient laboratory tests. We added these risk markers to demographic and Charlson Comorbidity Index models and 3 models from the Johns Hopkins Adjusted Clinical Groups system to predict individual cost, inpatient admission, and high-cost patients. We evaluated the predictive and discriminatory performance of 5 lab-enhanced models. Our study population incl...
To assess the effects of Hurricane Katrina on mortality, morbidity, disease prevalence, and servi... more To assess the effects of Hurricane Katrina on mortality, morbidity, disease prevalence, and service utilization during 1 year in a cohort of 20,612 older adults who were living in New Orleans, Louisiana, before the disaster and who were enrolled in a managed care organization (MCO). Observational study comparing mortality, morbidity, and service use for 1 year before and after Hurricane Katrina, augmented by a stratified random sample of 303 enrollees who participated in a telephone survey after Hurricane Katrina. Sources of data for health and service use were MCO claims. Mortality was based on reports to the MCO from the Centers for Medicare & Medicaid Services; morbidity was measured using adjusted clinical groups case-mix methods derived from diagnoses in ambulatory and hospital claims data. Mortality in the year following Hurricane Katrina was not significantly elevated (4.3% before vs 4.9% after the hurricane). However, overall morbidity increased by 12.6% (P <.001) compared with a 3.4% increase among a national sample of Medicare managed care enrollees. Nonwhite subjects from Orleans Parish experienced a morbidity increase of 15.9% (P <.001). The prevalence of numerous treated medical conditions increased, and emergency department visits and hospitalizations remained significantly elevated during the year. The enormous health burden experienced by older individuals and the disruptions in service utilization reveal the long-term effects of Hurricane Katrina on this vulnerable population. Although quick rebuilding of the provider network may have attenuated more severe health outcomes for this managed care population, new policies must be introduced to deal with the health consequences of a major disaster.
6026 Background: Many cancer survivors have comorbid conditions, adding complexity to their alrea... more 6026 Background: Many cancer survivors have comorbid conditions, adding complexity to their already complicated care and requiring greater care coordination. We assessed the role of care coordination in comorbid condition care for cancer survivors. Methods: Using SEER-Medicare, we examined 7 published indicators of quality comorbid condition care in survivors of loco-regional breast, prostate, or colorectal cancer who were diagnosed in 2004, in fee-for-service Medicare, and survived ≥3 years. Comorbid condition care was evaluated during the transition from initial cancer treatment to survivorship (i.e. days 366-1095 post-diagnosis). Coordination risk was categorized as Likely, Possible, or Unlikely using an index developed and tested as part of the ACG case-mix adjustment and predictive modeling tool. The index factors in the number of unique providers, number of specialties, the percentage majority source of care, and generalist visits. We tested the hypothesis that lower coordination risk would be associated with better comorbid condition care using logistic regression, adjusting for socio-demographics, cancer type, and comorbidity. Results: The sample included 8661 survivors (53% prostate, 22% breast, 26% colorectal; mean age 75; 65% male, 85% white). Our hypothesis was not supported. Compared to patients with Unlikely coordination issues, patients with Likely coordination issues were more likely to receive appropriate care on 4 indicators and less likely on 1. Possible coordination issues were associated with better care on 1 indicator and worse care on 1 indicator. To explore this finding further, we conducted post-hoc analyses examining the role of each component of the coordination risk index. Having more unique providers was generally associated with better comorbid condition care, in contrast to the calculation of the index which considers more unique providers a greater risk for coordination issues. Conclusions: These findings suggest that traditional metrics of care coordination may not be valid for survivors of cancer. Understanding the role of care coordination in cancer survivorship care requires development and application of alternative coordination measures.
IMPORTANCE This study assesses the role of telehealth in the delivery of care at the start of the... more IMPORTANCE This study assesses the role of telehealth in the delivery of care at the start of the COVID-19 pandemic. OBJECTIVES To document patterns and costs of ambulatory care in the US before and during the initial stage of the pandemic and to assess how patient, practitioner, community, and COVID-19-related factors are associated with telehealth adoption.
In early 2020, the CDC issued guidelines for personal COVID-19 mitigation behavior, such as mask-... more In early 2020, the CDC issued guidelines for personal COVID-19 mitigation behavior, such as mask-wearing, hand-washing, and social-distancing. We examine individual socio-behavioral factors that potentially predict mitigation compliance using public data. Our analysis finds that pandemic prompted strong mitigation behavior by adults, especially among females, non-whites, urban dwellers, and the psychological unwell. Other positive predictors were post-secondary education and higher income. Health symptoms and clinical risk factors did not predict increased mitigation practices, nor did age 65+ and proximity to infected persons. Our study findings are congruent with a report that pointed to a lack of increased pandemic mitigation practices in households with confirmed infections and health risks. We also point to lower levels of psychological resilience, lower socio-economic status, and non-urban location as potential explanatory factors for lack of mitigation behavior. Understanding what factors are associated with mitigation behavior will be important for policy makers in their efforts to curb the COVID-19 pandemic.
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Papers by Klaus Lemke