Healthcare Analytics

What is Healthcare Analytics?

Healthcare analytics is the systematic analysis of data to produce useful insights helping healthcare facilities improve their performance and quality of care for better patient experience and health outcomes. This data can be aggregated from patient, clinical and diagnostic data, as well as the financial and administrative data of a healthcare facility (i.e., hospital, healthcare system, physician network).

The amount of data information available to healthcare facilities and systems is vast. The challenge is in how to best use it. The application of data analytics transforms this mountain of data from information to actionable knowledge.

What is the Role of Data Analytics in Healthcare?

Data analytics informs healthcare facilities, systems and physicians in how to best make sense of the disparate streams of data information within their organizations. The steps of data analytics in healthcare include:

  • Collect raw data via data management and programming strategies
  • Process that data by organizing and “cleaning” (removing errors, corruptions, inconsistencies or outliers that can affect data accuracy)
  • Apply data analysis and visualization to look for patterns to gain knowledge
  • Turn this data-informed knowledge into actions that improve physician decisions, patient care quality and facility performance

In focusing on what information can be drawn and conclusions reached from the data gathered, health data analytics can be used to make sense of past results and outcomes and inform decisions for future actions.

The demand for data analytics in healthcare continues to increase, with 90% of healthcare executives surveyed rating the function as “very or extremely important” to their organizations for addressing reporting and quality improvement, population health initiatives, and accountable care.

What are the Types of Healthcare Analytics?

There are four types of healthcare analytics strategies that can be applied, based on the needs and goals of healthcare providers and facilities. Each is valuable for making the most of healthcare data, depending on the situation.

Descriptive Analytics

Collects and analyzes historical data. This is the most common analytics approach, as looking at data based on past and present decisions can help healthcare leaders make more informed decisions about the future.

Predictive Analytics

With a focus on the sharing and usage of biomedical data, biomedical informatics (or bioinformatics) translates a wide swath of biological systems to connect basic and clinical research with practical applications in healthcare.

Prescriptive Analytics

Uses advanced algorithms to help determine the effects of specific actions and provide solutions by applying historical data to “prescribe” the best action within a certain context. It is the most useful and most powerful type of analytics, providing guiding recommendations to healthcare professionals.

Discovery Analytics

Can help develop potential healthcare outcomes and identify avenues to be explored further. This “cutting edge” analytics approach uses historical data, forecasts and information from both inside and outside a healthcare organization. However, there can be limitations due to incomplete or inaccurate outside data.

How is Healthcare Analytics Used?

Healthcare analytics can be used to help healthcare organizations improve their performance and quality of care, thereby improving the patient health outcomes and overall experience. Healthcare analytics can be applied to these uses in several ways, from managing risk and reporting to advancing research initiatives.

Improving Healthcare Facility Performance​

Healthcare analytics can help healthcare facilities improve their performance and efficiency in both their business operations and in the patient care they provide.

In analyzing performance and larger healthcare trends, healthcare analytics is helping providers to better understand where to expand, what specialty services to invest in, and which current services to optimize. By detecting anomalies like false claims, redundant billing, or duplicate supply orders, healthcare analytics can help facilities save time, cut costs and eliminate waste.

One of the biggest areas of cost and waste can be inefficient supply management. Healthcare analytics can help to improve efficiency and streamline a healthcare facility’s supply chain by providing greater visibility and actionable insights on inventory levels, supply expenses and vendor relationships.

Improving Patient Experience and Health Outcomes

Integrating healthcare analytics with existing technologies and operational data can provide healthcare facilities and physicians with a greater level of data insight, leading to enhanced patient care and improved health outcomes.

For healthcare facilities and systems, Clinical Decision Support Systems (CDSS) are a key component for holistically applying their data. Using the insights delivered by healthcare analytics, a CDSS can improve the quality, safety, efficiency and effectiveness of healthcare services.

The overall goal of the system is to provide medical professionals and patients with accurate, timely information that leads to better patient outcomes. By getting all the relevant information about a patient, as well as codified standards about potential treatments, into a clinician’s hands in real-time, physicians have data-informed recommendations on the best next steps in a patient’s treatment. A CDSS can also alert medical providers to patient information they may not have known about and monitor for problems, such as potentially harmful medication interactions.

Healthcare Analytics Examples

Examples of the applications of healthcare analytics toward improved healthcare facility operations and efficiency, a better patient experience and overall improved patient outcomes are numerous and continue to expand as system and medical technology continue to evolve.

An example of one of the most important areas for improving a hospital’s efficiency, finances and patient outcomes is in reducing hospital readmissions. For the patient, readmission can be an indicator that they have not received the level of care needed to ensure full recovery. For the hospitals, a frequency of Medicare patients readmitted within 30 days or less can lead to government penalties. Using patient data compared against historical numbers on readmissions, hospitals can leverage predictive analytics to determine which patients are most likely to need readmission and look to solutions.

Just some of the examples of applying data analytics for better patient outcomes include:

  • Gathering and analyzing data on hospital ER wait times to gain insights on the number of patients who left without being seen, wait time effect on patient admittance and length of stay and impact on the level of patient satisfaction.
  • Tracking patient recovery time to determine average length of hospital stay based on diagnosis or treatment (i.e., heart surgery), ensuring patients are well enough to be discharged and that hospital beds are available for incoming patients.
  • Curbing the overuse of antibiotics in newborns through a predictive analytics algorithm to determine risk of neonatal infection by looking at the mother’s clinical data and the condition of the baby immediately following birth.
  • Applying healthcare analytics in population health management to determine the treatment, prevention and self-care of patients throughout a set population based on aggregated data.

How Do I Become a Health Data Analyst?

The health data analyst is the role tasked with examining and interpreting the data information and providing the insights that help physicians and healthcare facility leaders to develop the data-driven strategies and actions for improved operations, healthcare delivery and patient outcomes.

The educational requirements to enter the field is a bachelor’s degree, typically in health informatics, computer science, biological sciences or the medical fields with clinical experience. The additional education of a Graduate Certificate in Healthcare Analytics or earning a master’s degree with healthcare analytics concentration can provide professionals coming from these backgrounds (healthcare, clinicians, and IT professionals), with the working knowledge necessary to shape the collection, management, analysis, reporting and sharing of healthcare data to inform decision making.

What Skills are Needed for Healthcare Data Analytics?

Individuals working with health data information in the process of healthcare analytics must possess a deep understanding of data gathering, data storage and methods of data sharing, with the ability to skillfully interpret datasets as they relate to their organization’s needs and goals.

Health data analysts in particular should possess:

  • An expansive knowledge of structured query language (SQL)
  • Knowledge of and experience with handling electronic medical records
  • Knowledge of clinical trials processes and the data produced by them
  • Experience with the Python programming language
  • Experience working with systems developed by prominent health IT vendors

This technical knowledge and skill application are crucial, but healthcare analytics professionals also need “soft skills” to effectively interact with and influence others.

Valuable soft skills can include:

  • Communication skills
  • Negotiation skills
  • Strategic thinking
  • Problem solving
  • Leadership skills

Did you know healthcare analytics is an important part of health informatics? Health informatics encompasses healthcare analytics to achieve better patient outcomes by streamlining the sharing of information and fostering collaboration.

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