The Critical Role of Rich Healthcare Data
Recent global health challenges have delivered a sobering reminder that rich data—and the ability of healthcare providers and medical researchers to share that data—is critical to managing both individual and population health effectively. Rich data goes far beyond the patient information stored in electronic medical records (EMRs) to include structured and unstructured data either residing in other databases or being generated in real-time across multiple healthcare touchpoints.
When combating the spread of highly contagious viruses, healthcare providers and public health officials must know who has been exposed as soon as possible to prevent further transmission. In addition to testing and tracing data, they need comprehensive room capacity and medical device inventory data to manage resources during surges in cases. Access to timely, accurate information can mean the difference between effective containment and widespread outbreak.
Breaking Down Data Silos Through Digital Transformation
Historically, this critical data has been trapped in organizational silos, preventing healthcare systems from gaining a complete picture of patient health and population trends. However, digital transformation in healthcare is setting data free, enabling innovation and actionable insights through advanced analytics and the strategic use of artificial intelligence (AI) and machine learning (ML) technologies.
Leveraging AI and Machine Learning
Consider a hospital trying to project how many COVID-19 cases it can expect in a second wave. By applying ML and AI algorithms to rich, aggregated data sets, administrators could incorporate weather data and trends, mobile tracking data, population education data, socioeconomic indicators, and more. Analyzing and learning from these vast streams of data helps healthcare leaders determine where to allocate clinical, support, and outreach resources most effectively.
This intelligent approach to data analysis represents a paradigm shift from reactive to proactive healthcare management. Rather than simply responding to crises as they unfold, healthcare organizations can anticipate challenges and position resources strategically.
Real-World Applications of Aggregated Healthcare Data
Point-of-Care Information Access
Adding real-time and predictive data to the historical data found in EMRs allows healthcare providers to access information they need at the point of care and plan strategically for the future. This comprehensive data access is invaluable for ensuring clinical protocols are followed consistently across healthcare systems.
For example, hospitals could use integrated data systems to determine whether people who meet the criteria for virus testing every four weeks are being tested on schedule. If they aren’t, automated alerts can be sent to staff to book follow-up appointments, ensuring no patient falls through the cracks of the healthcare system.
Disease Management Through Metrics
To fight any contagious disease in a city, region, or country, healthcare professionals need metrics that help them understand disease patterns and progression. Key performance indicators include the number of people infected, that number aggregated by age demographics, and which individuals have any other disease that would put them in a high-risk group.
Having a comprehensive map of all these people gives providers information in real-time to quickly conduct targeted interventions. Geographic information systems (GIS) combined with health data enable precision public health responses that maximize impact while minimizing resource expenditure.
Predictive Analytics: The Future of Healthcare
The long-term payoff of rich data and smart technologies to healthcare providers lies in predictive data capabilities. Imagine how useful it would be to have data that can predict crowding in health facilities during a second virus wave, or accurately estimate the length of stay of a certain hospital patient, or assess the risk of sickness among particular cohorts of people or sections within a city, such as an area where there are a lot of elderly people with diabetes.
Running Simulations for Better Outcomes
This predictive capability can be accomplished by using historical rich data to run simulations about the impact of various educational and preventative initiatives. Historical data contains so much hidden information waiting to be uncovered through sophisticated analysis. That’s why this data must be freed from the silos and combined with structured and unstructured data aggregated from multiple sources.
By creating digital twins of healthcare systems and populations, administrators can test different intervention strategies virtually before implementing them in the real world. This approach reduces risk, saves resources, and ultimately saves lives through evidence-based decision-making.
Conclusion: Investing in Data Innovation
Beyond the immediate challenges of dealing with a worldwide outbreak of disease, sharing rich data is essential to the future care and treatment goals of healthcare organizations worldwide. Instead of investing millions of dollars in simply updating EMRs with newer versions of the same technology, hospitals should invest in innovative approaches that leverage the data they’ve already created in transformative new ways.
The future of healthcare depends not on collecting more data, but on breaking down barriers to sharing and analyzing existing data more intelligently. Healthcare organizations that embrace this data-driven approach will be better positioned to protect population health, optimize resource allocation, and deliver superior patient outcomes in an increasingly complex healthcare landscape.
