Addressing Data Management Challenges of COVID-19
In March 2020, hospital administrators across the country began receiving requests for daily reports of COVID-19 data for use in coordinating the federal government’s pandemic response. Data requests – including requirements for reports on testing, capacity, supplies, utilization, and patient flows – were the first of many needed to inform critical decisions about how to best manage the public health emergency.
As the pandemic has progressed, COVID-19 data has been essential to understanding the disease, keeping populations safe and giving communities the data they need to safely reopen. This information is essential not only to the federal government, but also to a long list of state, local and private parties – each with its own set of data needs and submission requirements. With hospitals facing historic staffing shortages, the challenge to meet the resulting demand for data entry, analytics and reporting has been overwhelming to say the least.
This was confirmed in a late 2020/early 2021 survey conducted by Healthcare Catalyst. In responding, most health system leaders said they weren’t prepared for the pandemic on the data and analytics front, with 60 percent stating that they didn’t have the data they needed for COVID-19 reporting.
The most reported gaps included the following:
- Government reporting data including test results, hospitalizations and deaths
- Missing data due to insufficient system integrations and unreliable data
- Data on bed, personal protective equipment (PPE) and supply utilization
- Predictive modeling/analytics for staffing and finance needs
Of the health systems that were not capturing the data needed for COVID-19 reporting, 88 percent said they had to change their operations and processes as they also worked to bridge gaps and update capabilities to capture missing patient data. Those with limited reporting capabilities and technology solutions found themselves implementing entirely new processes amid the pandemic.
Operational changes have included efforts such as:
- Creating systems to collect COVID-19 and other patient data
- Developing alternatives to document COVID-19 patients when EHR hasn’t updated
- Requiring additional data entry among staff
- Aggregating disparate data such as modifiers, denials and telehealth tracking
- Allowing flexibility in reporting with mix of automated and manual reports
- Creating additional reports with data analytics teams
- Supplementing internal capabilities with vendor partnerships
It’s clear is that the need for pandemic-related data and analytics is here to stay. Health systems can no longer rely on manual methods to collect and report on data needed for a complete, accurate and timely responses to information requests. Advanced tools such as machine learning can give healthcare systems the ability to leverage technology to recognize patterns within unstructured forms and convert it into actionable information.
New auto-indexing functionality from Vyne Medical, for example, uses form recognition to read incoming text data and automatically integrate it with the EHR – pre-populating patient information from high-volume forms like orders, insurance paperwork and patient registration documents. This information – along with recordings, documents and images linked to the patient – is made seamlessly accessible across the enterprise for easy retention, sharing and reference.
Transitioning from manual data entry and reporting to a fully digital process is the first step to conquering the challenges of data management. With digital technologies built to capture, automate and integrate healthcare data, health systems can begin to fight and win the data management battle.