A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study.

BACKGROUND: Disease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards. OBJECTIVE: We sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources. METHODS: Following a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems-a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds. RESULTS: Data from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability. CONCLUSIONS: We described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance.

Accuracy of the Veterans Health Administration COVID-19 (VACO) Index for predicting short-term mortality among 1307 US academic medical centre inpatients and 427 224 US Medicare patients.

BACKGROUND: The Veterans Health Administration COVID-19 (VACO) Index predicts 30-day all-cause mortality in patients with COVID-19 using age, sex and pre-existing comorbidity diagnoses. The VACO Index was initially developed and validated in a nationwide cohort of US veterans-we now assess its accuracy in an academic medical centre and a nationwide US Medicare cohort. METHODS: With measures and weights previously derived and validated in US national Veterans Health Administration (VA) inpatients and outpatients (n=13 323), we evaluated the accuracy of the VACO Index for estimating 30-day all-cause mortality using area under the receiver operating characteristic curve (AUC) and calibration plots of predicted versus observed mortality in inpatients at a single US academic medical centre (n=1307) and in Medicare inpatients and outpatients aged 65+ (n=427 224). RESULTS: 30-day mortality varied by data source: VA 8.5%, academic medical centre 17.5%, Medicare 16.0%. The VACO Index demonstrated similar discrimination in VA (AUC=0.82) and academic medical centre inpatient population (AUC=0.80), and when restricted to patients aged 65+ in VA (AUC=0.69) and Medicare inpatient and outpatient data (AUC=0.67). The Index modestly overestimated risk in VA and Medicare data and underestimated risk in Yale New Haven Hospital data. CONCLUSIONS: The VACO Index estimates risk of short-term mortality across a wide variety of patients with COVID-19 using data available prior to or at the time of diagnosis. The VACO Index could help inform primary and booster vaccination prioritisation, and indicate who among outpatients testing positive for SARS-CoV-2 should receive greater clinical attention or scarce treatments.

Modeling the Potential Economic Impact of the Medicare Comprehensive Care for Joint Replacement Episode-Based Payment Model.

BACKGROUND: The Medicare program has initiated Comprehensive Care for Joint Replacement (CJR), a bundled payment mandate for lower extremity joint replacements. We sought to determine the degree to which hospitals will invest in care redesign in response to CJR, and to project its economic impacts. METHODS: We defined 4 potential hospital management strategies to address CJR: no action, light care management, heavy care management, and heavy care management with contracting. For each of 798 hospitals included in CJR, we used hospital-specific volume, cost, and quality data to determine the hospital's economically dominant strategy. We aggregated data to assess the percentage of hospitals pursuing each strategy; savings to the health care system; and costs and percentages of CJR-derived revenues gained or lost for Medicare, hospitals, and postacute care facilities. RESULTS: In the model, 83.1% of hospitals (range 55.0%-100.0%) were expected to take no action in response to CJR, and 16.1% of hospitals (range 0.0%-45.0%) were expected to pursue heavy care management with contracting. Overall, CJR is projected to reduce health care expenditures by 0.5% (range 0.0%-4.1%) or $14 million (range $0-$119 million). Medicare is expected to save 2.2% (range 2.2%-2.2%), hospitals are projected to lose 3.7% (range 4.7% loss to 3.8% gain), and postacute care facilities are expected to lose 6.5% (range 0.0%-12.8%). Hospital administrative costs are projected to increase by $63 million (range $0-$148 million). CONCLUSION: CJR is projected to have a negligible impact on total health care expenditures for lower extremity joint replacements. Further research will be required to assess the actual care management strategies adopted by CJR hospitals.