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C19 20-212 – HSR&D Study

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C19 20-212
Rapid assessment of national surges and variations in COVID-19 inpatient nurse staffing using a big data approach.
Laura A Petersen MD MPH
Houston, TX
Funding Period: June 2020 - March 2021

BACKGROUND/RATIONALE:
Hospitals are developing plans for handling the surge of patients seeking care for COVID-19 and corresponding strains on health care resources, including staff, personal protective equipment (PPE), and respiratory support equipment. This project will address the HSR&D priority area of staffing: new approaches needed to respond to the outbreak, efficient ways of expanding or shifting staff capacity, and models for dealing with staff shortages due to illness/quarantine. Difficulty in determining how many staff members are in direct patient care roles at the hospital level and at the inpatient unit level has been an ongoing challenge for both research and operations applications. As part of VA-funded work (HSR&D IIR 15-438) examining how inpatient resources and burden affect outcomes, our team has developed methods for assessing nurse staffing and workload using staff activity data in the electronic health record (EHR), allowing for identification of staff (e.g., registered nurses [RNs]) in direct patient care roles and also where in the hospital these staff are located at particular times. The EHR data are consistent across all hospitals in the VA system and collected as part of routine care, making comparisons across the entire system possible.

OBJECTIVE(S):
We aim to adapt our methods developed using historical data to real-time or near real-time data and then to work with operational partners to make staffing monitoring tools available for national, network, hospital, and/or unit leaders.
Aim 1: Adapt methods for assessing inpatient staffing to real-time or near-real-time data and provide reports to operational leaders
Aim 2: Obtain input from operational leaders on their preferences for staffing report features and incorporate prioritized and feasible items into staffing reports

METHODS:
This project will utilize electronic health record (EHR) data from the VA Corporate Data Warehouse for all VHA acute-care inpatient units, as well as the Nurse Unit Mapping Application (to link bed locations with nursing units) and Human Resources Information Service data (to link staff with their respective occupations).
Aim 1
1a: Use bar code medication administration (BCMA) data to estimate staffing and workload. Preliminary data show that over 90% of VA units have a peak time for scheduled medications at 9:00 am or 10:00 am. We will determine the number of unique nurses administering medications each day and the number of nurses administering medications scheduled for the peak scheduled medication time each day. We will also determine the number of patients to whom nurses administered medications. In addition, we will determine the total time it takes for nurses to administer medications to all of their patients who have medication scheduled for the peak time.
1b: Estimate direct-care RN hours worked. We will adapt for use with (near) real-time data a previously developed method for estimating the number of direct-care hours worked per inpatient RN based on activity documented in the electronic health record (BCMA, vital signs, notes). We estimate direct-care hours for each RN by adding up all 2-hour blocks in which the RN has at least one of the indicated activities in the health record and bridging any 2-hour blocks in which the RN has activity in both the preceding and following 2-hour block.
1c: Prepare reports using (near) real-time data for operational leaders for monitoring staffing. Data for reports will be aggregated at the unit and facility level and show trends in daily and/or shift-level nurse staffing and workload.
Aim 2
Virtually convene or correspond on at least a monthly basis with operational partner representatives to determine their needs for rapid development of predictive models related to inpatient staffing

FINDINGS/RESULTS:
The project will start in June 2020 and will report findings at a later date.

IMPACT:
Monitoring and modeling of temporal and regional variation in direct-care nurse staffing and workload can support deployment of resources to areas of greatest Veteran need and examination of the impact of varying staffing patterns on outcomes. Tracking changes in staffing levels and workload will be essential to developing appropriate responses to pandemics and other catastrophic stresses to the health care delivery system.

PUBLICATIONS:
None at this time.

DRA: Health Systems
DRE: None at this time.
Keywords: Data Management
MeSH Terms: None at this time.

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