What is the Role of Data Analytics in Healthcare?

Healthcare is a quickly-evolving industry; it is continuously adjusting to society’s needs at large and changing with the growth of technology, telecommuting, and globalization.¹ One thing that has not changed with healthcare is the need for information, at a broader scale, and at the patient level. Learning clinical data analysis and health information management may open up whole new avenues of patient care and advancements in medical procedures. This is where the data analyst roles and responsibilities come into play. What is data analytics in healthcare, and how important is it in the healthcare administration and industry?? Read on to learn more.

Understanding Data Analytics

Data analytics is a broad practice that refers to taking in large amounts of data and analyzing them for potential insights. The health field has historically produced large amounts of clinical data and medical records stored in hard copy form. The advent of technology has allowed that data to become digitized, allowing for new forms of analysis driven by the growth of new software and hardware²

Healthcare analytics has created many data analyst careers and can be broken down into subcategories, including big data analytics, public health analytics, and predictive analytics.

Big Data Analytics

Big data analytics in the healthcare industry refers to massive electronic data sets that are so complex and diverse that they are nearly impossible to analyze. Part of this difficulty in data management comes from the sheer diversity of data sources. Where much of the data initially came from electronic health records (EHR), today’s totality of big data related to patient healthcare includes:

• Written notes and prescriptions from physicians
• Medical imaging and lab tests
• Monitoring vital signs and other machine-generated data
• Administrative data
• Insurance information
• Social media posts

Compiling all of this data comes with its challenges, but big data analytics can potentially contribute to massive changes and advancements. Because healthcare analysts are also adept in data visualization, the acquired big data may help healthcare organizations provide preventative care, support disease surveillance, and develop new diagnostic and clinical techniques.³

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Public Health Analytics

While individual patient care is essential, supporting public health can reduce individual cases and develop more proactive health solutions within communities. Public health analytics refers to obtaining, reviewing, and managing the health-related data of larger local populations. This may include:

• Mortality reports
• Demographic data
• Socioeconomic data
• Medical claims

Using this data to leverage health science, public health organizations can potentially monitor disease trends, determine the progression of diseases within populations, and prioritize communities that may need health resources.

Predictive Analytics

Predictive analytics offers an even more advanced form of healthcare data analytics. It mainly takes historical and real-time data to predict potential outcomes. This can help healthcare providers identify trends or patterns that can then be applied to patient care and outreach.⁴

Using Data Analytics in Healthcare

The actual applications for data analytics are vast and can potentially apply to every healthcare component, from patients to doctors to communities at large. Whether you have a certificate in healthcare data analytics or a healthcare analytics degree, the training will provide valuable expertise in the healthcare field.

Patient Costs

The cost of healthcare in the United States remains a significant obstacle for many populations, but big data analytics may contribute to substantial cost savings for providers and patients alike.⁵ This comes thanks to:

• Faster time to treatment – health data analytics can provide a quicker, more precise diagnosis while having more operational efficiency. This allows for more informed decisions and efficient treatment regimens while reducing the amount of time that patients spend in waiting rooms.
• Reduced readmissions – As mentioned, patient data analytics can play a role in preventive care. In practice, this may mean sending automatic notifications to patients when it is time for a booster shot or a checkup. This can allow providers to manage health before they become an emergency, which can reduce costs for patients.
• Medication therapy management – Medication therapy management involves helping patients better understand their health and the medications designed to support their health. Having patients stick with and properly take their medication can come with hurdles that could contribute to emergencies and increased health costs. Data analytics may help pharmacists and providers manage patient drug therapies in real-time. This can reduce readmissions, hospitalizations, and emergency visits, which means cost savings overall.

Evaluating Performance

Patients should be getting the best care available at all times. The next time you are researching a healthcare analyst job description,  a masters in healthcare analytics program,  or a healthcare data analyst salary, remember that you are also helping millions of people get accurate healthcare.  With data analytics, providers can evaluate individual practitioners’ performance, ensuring that they are providing efficient and effective care. Data analytics can combine regular performance evaluations with health data and medical data to provide feedback to practitioners, ultimately helping them improve, affecting patient satisfaction and overall quality of care.

For example, the Ongoing Professional Practice Evaluation (OPPE) evaluates practitioner performance using direct observation combined with data from practice patterns, patient complaints, outcomes, and resource usage. The aggregation of data can then be compared with other qualitative performance measurements, like interpersonal communication skills and professionalism, to determine medical professionals' performances. Constant real-time data collection and analytics can be used to track performance and identify areas of improvement for medical professionals.

Predictive Analytics and Personalized Treatment

Along with its potential cost savings, predictive analytics could offer more personalized treatment. Usually, predictive analytics uses large amounts of data aggregated from millions of patients to support public health. However, for certain patients, providers can gather and analyze data to determine the risk of developing chronic diseases. This may come from biometric data, lab tests, family health history, and patient-generated health data, including data from health tracking apps. From there, providers can present changes to lifestyle, wellness activities, and enhanced medical services to support a patient’s long-term health.

Data Analytics and Telemedicine

Telemedicine is a growing field in medicine that can take various forms but is most often associated with videoconferencing between patients and doctors. This allows for increased accessibility to healthcare services and reduced costs. Remote patient monitoring and remote clinical services enable doctors to gather real-time data using smart devices and cloud health information systems. Over time, providers can analyze the data and alert physicians of any potential problems. Doctors may be able to detect acute medical events and emergencies before they even happen.⁶

Proactive, Holistic Care

Data analytics can most importantly contribute to more proactive care that makes health an everyday priority instead of a once-in-a-while interruption. Data science can play a role in supporting a healthy lifestyle, keeping up with regular checkups, and taking care of all aspects of health, including mental and emotional health.

Potential Challenges with Data Analytics in Healthcare

One of the most prominent challenges with data of any kind is security and privacy. This becomes even more important, given the sensitivity and importance of personal health data. This has led to a reluctance by both patients and providers to make electronic health records and data accessible, which then poses a barrier in obtaining high-quality data. ⁷

Further challenges come with the stratification of patient management, which is often split between the healthcare provider and the insurance provider. Insurers care almost entirely about costs, which means that they are often at odds with patients and, by extension, healthcare providers. This can potentially create skews in data that insurers can take advantage of.

Data analytics and health informatics have numerous current and potential uses in healthcare that will only grow with new technological advancements. Given the right care and safeguards, data analytics can become a huge benefit for patients and physicians and develop whole new processes for all aspects of individual and public health.

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Sources

1. “5 Out of 20 Fastest-Growing Industries from 2019 to 2029 Are in Healthcare and Social Assistance.” U.S. Bureau of Labor Statistics. U.S. Bureau of Labor Statistics. Accessed November 23, 2021. https://www.bls.gov/opub/ted/2020/5-out-of-20-fastest-growing-industrie…;
2. “The Role of Data Analytics in Health Care.” School of Health and Rehabilitation Sciences Online - University of Pittsburgh. Accessed November 23, 2021. https://online.shrs.pitt.edu/blog/data-analytics-in-health-care/. 
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