Predicting High-risk and High-cost Patients for Proactive Intervention.

BACKGROUND: It is well known that 20% of the patients incur 80% of health care costs and many diseases and complications can be prevented or ameliorated with prompt intervention. One of the well-recognized strategies for cost reduction and better outcomes is to predict or identify high-risk and high-cost (HRHC) patients for proactive intervention. OBJECTIVE: The objective of this study was to develop a predictive model that can be used to identify HRHC patients more accurately for proactive intervention. METHODS: This is an observational study using fiscal year (FY) 2018 administrative data to predict FY 2019 total cost at the patient level. All 5,676,248 patients who received care in both FYs 2018 and 2019 from the Veterans Health Administration were included in the analyses. The Veterans Health Administration Corporate Data Warehouse was our main data source. With split-sample analyses, 3 sets of patient comorbidities and 5 statistical models were assessed for the highest predictive power. RESULTS: The Box-Cox regression using comorbidities designated by the expanded CCSR (Clinical Classifications Software Refined) groups as predictors yielded the highest predictive power. The R2 reached 0.51 and 0.37 for the transformed and raw scale cost, respectively. CONCLUSIONS: The predictive model developed in this study exhibits substantially higher predictive power than what has been reported in the literature. The algorithm based on administrative data and a publicly available patient classification system can be readily implemented by other value-based health systems to identify HRHC patients for proactive intervention.

The Application of a Hospital Medical Surge Preparedness Index to Assess National Pandemic and Other Mass Casualty Readiness.

EXECUTIVE SUMMARY: This article describes the use and findings of the Hospital Medical Surge Preparedness Index (HMSPI) tool to improve the understanding of hospitals' ability to respond to mass casualty events such as the COVID-19 pandemic. For this investigation, data from the U.S. Census Bureau, the Dartmouth Atlas Project, and the 2005 to 2014 annual surveys of the American Hospital Association (AHA) were analyzed. The HMSPI tool uses variables from the AHA survey and the other two sources to allow facility, county, and referral area index calculations. Using the three data sets, the HMSPI also allows for an index calculation for per capita ratios and by political (state or county) boundaries. In this use case, the results demonstrated increases in county and state HMSPI scores through the period of analysis; however, no statistically significant difference was found in HMSPI scores between 2013 and 2014. The HMSPI builds on the limited scientific foundation of medical surge preparedness and could serve as an objective and standardized measure to assess the nation's medical readiness for crises such as the COVID-19 pandemic and other large-scale emergencies such as mass shootings. Future studies are encouraged to refine the score, assess the validity of the HMSPI, and evaluate its relevance in response to future legislative and executive policies that affect preparedness measures.

Development of a Hospital Medical Surge Preparedness Index using a national hospital survey.

To generate a Hospital Medical Surge Preparedness Index that can be used to evaluate hospitals across the United States in regard to their capacity to handle patient surges during mass casualty events. Data from the American Hospital Association's annual survey, conducted from 2005 to 2014. Our sample comprised 6239 hospitals across all 50 states, with an annual average of 5769 admissions. An extensive review of the American Hospital Association survey was conducted and relevant variables applicable to hospital inpatient services were extracted. Subject matter experts then categorized these items according to the following subdomains of the "Science of Surge" construct: staff, supplies, space, and system. The variables within these categories were then analyzed through exploratory and confirmatory factor analyses, concluding with the evaluation of internal reliability. Based on the combined results, we generated individual (by hospital) scores for each of the four metrics and an overall score. The exploratory factor analysis indicated a clustering of variables consistent with the "Science of Surge" subdomains, and this finding was in agreement with the statistics generated through the confirmatory factor analysis. We also found high internal reliability coefficients, with Cronbach's alpha values for all constructs exceeding 0.9. A novel Hospital Medical Surge Preparedness Index linked to hospital metrics has been developed to assess a health care facility's capacity to manage patients from mass casualty events. This index could be used by hospitals and emergency management planners to assess a facility's readiness to provide care during disasters.

Public health interventions and epidemic intensity during the 1918 influenza pandemic.

Nonpharmaceutical interventions (NPIs) intended to reduce infectious contacts between persons form an integral part of plans to mitigate the impact of the next influenza pandemic. Although the potential benefits of NPIs are supported by mathematical models, the historical evidence for the impact of such interventions in past pandemics has not been systematically examined. We obtained data on the timing of 19 classes of NPI in 17 U.S. cities during the 1918 pandemic and tested the hypothesis that early implementation of multiple interventions was associated with reduced disease transmission. Consistent with this hypothesis, cities in which multiple interventions were implemented at an early phase of the epidemic had peak death rates approximately 50% lower than those that did not and had less-steep epidemic curves. Cities in which multiple interventions were implemented at an early phase of the epidemic also showed a trend toward lower cumulative excess mortality, but the difference was smaller (approximately 20%) and less statistically significant than that for peak death rates. This finding was not unexpected, given that few cities maintained NPIs longer than 6 weeks in 1918. Early implementation of certain interventions, including closure of schools, churches, and theaters, was associated with lower peak death rates, but no single intervention showed an association with improved aggregate outcomes for the 1918 phase of the pandemic. These findings support the hypothesis that rapid implementation of multiple NPIs can significantly reduce influenza transmission, but that viral spread will be renewed upon relaxation of such measures.

Unexplained metabolic acidosis in critically ill patients: the role of pyroglutamic acid.

OBJECTIVE: To determine the role of pyroglutamic acid (PGA) in the pathogenesis of unexplained metabolic acidosis in critically ill patients. DESIGN AND SETTING: Case series in the medical ICU of an urban hospital. PATIENTS: 23 patients admitted to the medical ICU with acidemia (pH <7.35 or HC0(3) < or = 16 mEq/l) not explained by the presence of ketoacidosis, lactic acidosis, renal failure or ingestion of drugs or toxins and who had an increase in the strong ion gap (SIG) greater than 5. MEASUREMENTS AND RESULTS: Plasma levels of sodium, potassium, chloride, bicarbonate, calcium (ionized), magnesium, lactate, phosphate, albumin, blood urea nitrogen, and creatinine were measured. Arterial blood gases and urine dipstick for ketones were also analyzed. Plasma was assayed for PGA using gas chromatography. The patient's history and Kardex were reviewed for evidence of acetaminophen administration. The plasma PGA level was found to be very low in all patients studied. The correlation between SIG and PGA (r) was -0.01 (95% CI: -0.42 to 0.40). PGA therefore did not account for the observed increase in the SIG. There appeared to be no obvious influence of acetaminophen intake on levels of PGA in the plasma. CONCLUSIONS: We were unable to confirm the importance of PGA as a cause of unexplained metabolic acidosis and increased SIG in our critically ill patients.