Taking a Data-Driven Approach to Preventing Outbreaks

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A data analytics professional briefs her team of analysts.

Analytics professionals who collect and analyze data help organizations make decisions. In the health care setting, these professionals also help save lives.

Using a data-driven approach, public health officials and those in health information roles can identify potential outbreaks or various diseases, which can be critical in developing strategies to either prevent or minimize outbreaks. Leveraging epidemiological data also helps to evaluate and track the spread of outbreaks, such as the ongoing COVID-19 pandemic, and can improve patient outcomes for health care facilities.

Why a Data-Driven Approach to Health Care Matters

Health care facilities with the latest data and analytics can improve patient outcomes. Health information is gathered across the health care setting through health information systems. Used by clinicians, patients, and public health officials, these systems store data from providers and health organizations and are leveraged to improve patient outcomes, develop research, and influence those who make decisions and policies.

Examples of health information systems include:

  • Electronic medical records (EMRs). Electronic medical records are easily accessible, online versions of a patient’s medical history, which include personal health data, test results, and treatments.
  • Medical billing software. Medical billing software automates and streamlines the process of patient billing, tracking, and payment.
  • E-prescribing software. Medical office staff can manage and track the prescription process for their patients by sending prescriptions directly to pharmacies to be filled, reducing the possibility of errors.
  • Master patient index (MPI). The MPI serves as the master record for the patient. It connects and indexes patient records across databases to reduce the likelihood of duplicate records and inaccurate patient data.
  • Patient portals. Through their online portal, patients are empowered to manage their care. They can communicate with their physicians and nurses and access their lab result histories, medications, and appointment information.
  • Remote patient monitoring (RPM). RPM allows health care professionals to provide remote care to patients, particularly those who are homebound with chronic health conditions, through video appointments and the use of medical sensors.
  • Clinical decision support. This system analyzes data from clinical and administrative systems. It helps physicians to diagnose patients and predict drug interactions and other medical events.

Benefitting from Integrated Data

There are several benefits to integrating data-driven tactics into health care strategies.

Health information systems focus on efficient records management, robust and accurate patient information, and data management, driven by:

  • Data analytics. Health data is gathered, compiled, and analyzed to manage population health, reduce health care costs, and improve patient care.
  • Cost reductions. Health information exchanges have been found to lower costs and reduce the likelihood of duplicate procedures and unnecessary imaging, according to the Journal of the American Medical Informatics Association.
  • Population health management. Aggregated patient health information helps to predict or prevent outbreaks and identify population patterns and trends.
  • Quality management. Measuring health care quality requires accurate data from medical records and health care facility and provider databases used for paying bills and managing patient care.

Properly analyzing information derived from data-driven systems can lead to health care strategies that are more personalized, are operationally efficient, and can improve a facility’s care delivery system.

Examining Health Care Data and Outbreaks

Analytics professionals turn vast amounts of structured and unstructured data into insights to tell a story. They work in tech-focused high-demand roles as data scientists, business analysts, and machine-learning engineers. In the health care setting, they apply a data-driven approach to examining factors that may lead to outbreaks or those that could greatly undermine public health.

This is achieved through health surveillance systems:

  • Provide an early warning system to identify public health emergencies
  • Document the impact of interventions or progress toward public health goals
  • Monitor the epidemiology of a condition to set priorities and inform public health policy and strategies

Health-related data is collected, analyzed, and interpreted on an ongoing basis to plan, implement, and evaluate public health practices. The World Health Organization provides recommended standards for surveillance of vaccine-preventable diseases and makes data available in a consolidated format.

When an outbreak occurs in the U.S., the Centers for Disease Control and Prevention (CDC) partners with health departments and federal agencies such as the U.S. Food and Drug Administration to protect patients and stop outbreaks from spreading in health care facilities. Following what’s known as an Epi-Aid investigation, the CDC advises the public about what they can do to protect themselves, provides recommendations to the medical and public health community about how to prevent future infections, and works closely with policymakers, regulatory agencies, and relevant industries to learn how to prevent future outbreaks.

The Data-Driven Approach in Action

Health care administrators are leveraging a data-driven approach to evaluate and track the spread of the ongoing COVID-19 pandemic. The availability of public datasets is important in the early stages of an outbreak, according to The Lancet. Readily available data paves the way for analytical efforts by independent teams and provides robust evidence to guide interventions.

Machine-learning models are being used to help measure an individual’s clinical risk, the probability for intensive care, and the likelihood of death. This data-driven approach looks at individuals’ basic medical history and takes into account the severity of their symptoms, age, presence of comorbidities such as diabetes or hypertension, and other data, according to Harvard Business Review.

Because accessibility to comprehensive medical records is limited, the data required to assess an individual’s clinical risk of contracting a given virus are not easily accessed, according to Harvard Business Review. It would take time for patterns to emerge between the historical data in medical records and the impact of the virus on its victims.

Facing Challenges Beyond Medical Data

Caused mostly by a lack of data-sharing across the health care system, data silos are largely to blame for the lack of access to behavioral, demographic, and interaction data, according to Medical Product Outsourcing. Other challenging factors are incomplete data input, lack of integration with advanced matching capabilities, and real-time access when health conditions change rapidly.

Leveraging Data to Prevent Outbreaks

A data-driven approach to health care helps facilities improve patient outcomes and public health officials better identify potential outbreaks. Discover how a career as a data analysis professional serves an important role in public health. Focus on core areas of study such as predictive analytics and business intelligence to help mitigate the risk of future pandemics. Learn a variety of advanced analysis tools, techniques, and methodologies through Ohio University’s Online Master of Business Analytics program.

Recommended Readings

Data Mining in Business: What Is It, and How Can It Be Used?

Big Data Analytics Tools and Resource for Identifying Emerging Business Trends

Student Interview: Online Master of Business Analytics

Sources:

Agency for Healthcare Research and Quality, Data Sources for Health Care Quality Measures
Centers for Disease Control and Prevention, Outbreak Investigations in Healthcare Settings
Digital Guardian, “What Is a Health Information System?”
Harvard Business Review, “Leveraging AI to Battle This Pandemic — and the Next One”
Journal of the American Medical Informatics Association, “The Benefits of Health Information Exchange: An Updated Systematic Review”
The Lancet, “Early Epidemiological Analysis of the Coronavirus Disease 2019 Outbreak Based on Crowdsourced Data: A Population-Level Observational Study”
Medical Product Outsourcing, “The Benefits of a Data-Driven Approach to the Patient Experience”
PLoS ONE, “An Open-Data-Driven Agent-Based Model to Simulate Infectious Disease Outbreaks”
World Health Organization, Public Health Surveillance