4 Benefits of Data Analytics in Healthcare
- What is data analytics in healthcare?
- What are the benefits of data analytics in healthcare?
- Applying health data to improve patient outcomes
- The amount of data in the world grew by 5,000% between 2010 and 2020.
- The total amount of data generated each year doubles about every three years and is estimated to reach 79 zettabytes in 2021.
- Netflix’s use of predictive analytics has allowed the streaming service to influence 80% of the movies and TV shows that its 100 million subscribers selected.
What is data analytics in healthcare?
- Making healthcare data easier to share among colleagues and external partners, and easier to visualize for public consumption
- Providing accurate data-driven forecasts in real time to allow healthcare providers to respond more quickly to changing healthcare markets and environments
- Enhancing data collaboration and innovation among healthcare organizations to convert analytics-ready data into business-ready information by automating low-impact data management tasks
- Software that acquires the data from sources that include patient surveys, case files, and machine-to-machine data transfers
- Programs that clean, validate, and analyze the data in response to a specific research question
- Software that builds on the results of the analysis to suggest various actions to achieve specific healthcare goals
How data analytics is used in healthcare settings
- Research and prediction of disease
- Automation of hospital administrative processes
- Early detection of disease
- Prevention of unnecessary doctor’s visits
- Discovery of new drugs
- More accurate calculation of health insurance rates
- More effective sharing of patient data
- Personalization of patient care
Who performs data analytics for healthcare applications?
- Clinical practitioners. Data analytics in clinical settings attempts to reduce patient wait times via improved scheduling and staffing, give patients more options when scheduling appointments and receiving treatment, and reduce readmission rates by using population health data to predict which patients are at greatest risk.
- Healthcare payers. Insurance firms use data analytics to confirm that they comply with ever-changing regulations; analyze claims and prescriptions to target prevalent health maladies; and compare pricing data with quality metrics to identify high-value, low-cost health providers. Insurance firms also use predictive analytics to spot the potential for fraudulent claims and notify providers of at-risk claims.
- Population health managers. Public health professionals increasingly emphasize prediction and prevention over response and treatment. Predictive analytics is used to identify patients at highest risk of chronic illness at the early stages of the disease. Analysis of lab testing, claims data, data that patients themselves generate, and various social factors reduces the risk of long-term illness, which lowers overall healthcare costs and improves patient outcomes.
What are the benefits of data analytics in healthcare?
- Integrate heterogeneous data types.
- Ensure the quality of the data upon reception and throughout the analysis.
- Create data models.
- Interpret the results of the analysis.
- Validate the analysis results.
- Support clinical treatment decisions from physicians and other health professionals.
- Improve the accuracy and speed of identifying patients at highest risk of disease.
- Provide greater detail in the EHRs of individual patients.
- Make the provision of healthcare more efficient, which reduces costs.
- Promote preventive measures by giving patients greater insight into their health and treatment goals.
- Integrate data from consumer fitness devices and other patient-provided sources of health data.
- Deliver real-time alerts to healthcare providers by analyzing health data at the collection point.
How data analytics is used to advance medical research
- Data scientists from Blue Cross Blue Shield and analytics firm Fuzzy Logix have identified 742 risk factors that accurately predict when a person is at risk of abusing opioids.
- The Cancer Moonshot initiative that former President Barack Obama initiated in his second term relies on advanced data analytics techniques to speed the discovery of cancer cures by identifying the trends and treatments used worldwide that have the highest success rate.
- The research collaboration firm OptumLabs has created a database of more than 30 million EHRs that doctors can access to support their treatment decisions. The database has proven to be especially helpful in treating patients who have complex medical histories or who suffer from multiple conditions.
- AI-based analytics promises to allow radiologists to “read” images via algorithms that can more accurately identify patterns that indicate a particular diagnosis.
- Kaiser Permanente worked with the Mental Health Research Network to analyze EHRs and the results of a standard depression questionnaire to identify with great accuracy patients at highest risk of attempting suicide.
Benefit #1: Analyzing clinical data to improve medical research
- EHRs combine a patient’s X-rays and other medical images, diagnoses, treatment plans, allergies, and test results in standard digital formats. This makes the information easy to share but introduces privacy and regulatory compliance requirements that limit how the data may be used.
- Electronic medical records are similar to EHRs but include only information from the patient’s paper charts created in medical offices, clinics, and hospitals. They are used primarily for diagnosis and treatment; their main value is in tracking a patient’s healthcare over years of visits and screenings.
- Personal health records maintain a history of the patient’s health treatment that the patient keeps rather than healthcare providers. The records are intended to assist in the patient’s own health management and don’t legally replace the medical records that healthcare providers maintain.
- Public health records are among the most promising sources of health data for medical research. For example, the National Cancer Institute’s Cancer Research Data Commons (CRDC) serves as a cloud-based data science platform that links data analytics tools with data repositories storing genomic, proteomic, comparative oncology, imaging, and other data types.
- The results help researchers identify approaches to improve the efficiency of clinical processes and other healthcare operations.
- The research leads to more accurate diagnosis and treatment by personalizing healthcare provision.
- Cohort studies provide medical researchers with new insight into the causes of disease by linking risk factors with health outcomes.
Benefit #2: Using patient data to improve health outcomes
- The health outcomes that patients expect and that matter most to them
- How the processes that healthcare providers use impact patients’ desired outcomes
- How the resources, equipment, regulations, and other aspects of healthcare infrastructure affect the quality of care that patients receive
- Recovery from acute illness
- Living well while managing a chronic condition
- Maintaining dignity at the end of life
Benefit #3: Gaining operational insights from healthcare provider data
- Waste reduction. The annual cost of recoverable waste in the U.S. healthcare industry is estimated at $1 trillion. Much of the waste is due to variations in clinical practices, inappropriate care, preventable care-related injury and death, and failure to follow proven procedures.
- Cost-effective use of technology. Adopting analytics and other advanced technologies is expensive and time consuming. A framework for cost-effectively implementing data analytics focuses on measuring, understanding, and improving those operations with the most promise for improved outcomes.
- Increasing hospital capacity. A less-expensive alternative to building new healthcare capacity is to apply analytics techniques to better manage demand for hospital beds and other healthcare resources.
- Improved project management. As healthcare organizations adopt the Institute for Healthcare Improvement’s Triple Aim framework for enhanced patient outcomes and operational efficiency, project management becomes an integral part of cost control, risk management, and successful process improvement.
- Sustaining outcome improvements. Efforts to implement data-driven enhancements to healthcare operations often underestimate the resources needed to sustain the improvements. To ensure the long-term success of analytics efforts, organizations should engage all stakeholders at every stage of the process.