What is Healthcare Data Analytics?

Frequently Asked Questions (FAQs)

Contributing Changes to the Increasing Adoption Rates of Healthcare Data Analytics

Healthcare data analytics is a set of activities used to process data that is collected within the healthcare environment. In the United States, the healthcare data analytics industry is expected to reach a value of $31 billion by 2022, with an increasing number of hospitals and healthcare organizations leveraging big data for a variety of applications. Increasing adoption rates of healthcare data analytics is a result of three ongoing changes that are taking place in global technology:

  1. Data is becoming more available. For the past two decades, humans have increased the total number of data produced and stored at exponential rates and the healthcare industry is no exception. Regulations such as the electronic medical records (EMR) mandate have resulted in the widespread digitization of patient data in the past decade, and the increasing presence of connected devices and embedded computers in health care environments are also being leveraged to collect clinical patient data.
  2. Data storage is becoming more available. Advances in data storage technology have made it easier for more healthcare organizations to access the data storage needed to leverage healthcare data analytics at an affordable price. In the past, a hospital would have to undertake huge up-front costs to build and manage its own data center. Today, the cost per gigabyte of data storage has fallen considerably from where it was 10 or 20 years ago, and healthcare organizations can also outsource big data storage to cloud service providers who provide flexible and effectively limitless storage at an affordable price point.
  3. Data processing is becoming more available. Advances in computer processing power and data processing algorithms mean that computers can now process greater volumes of data in less time than ever before, generating insights and information along the way that would be impossible for a human analyst to substantiate in a reasonable period.

The increased availability of data, the ability to store large volumes of data and the ability to process large volumes of data are driving increased data collection in health care environments and enabling rapid innovation in healthcare data analytics.

Healthcare Data Analytics: Four Types of Data

Healthcare data analytics uses specialized programs and algorithms to sift through large volumes of health care information, but where does that information come from? It is useful to understand healthcare data as belonging to one of four categories:

Claims and Cost Data – This type of data can be harvested from insurance claim forms when a health insurance plan member submits a claim. Claims and costs can be correlated by demographics to identify opportunities for intervention or education to improve outcomes.

Pharmaceutical and R&D Data – This type of data includes data from drug efficacy trials and clinical research data that can be used for predictive modeling and other applications.

Clinical Data – Clinical data is recorded by physicians during their interactions with patients and added to patient medical records. The EMR mandate requires healthcare organizations to keep these records in digital format, making it accessible for use in healthcare data analytics.

Patient Behavior and Sentiments – In the United States, the Centers for Medicare & Medicaid Services (CMS) collects data on patient sentiments through a survey known as the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). The data is analyzed by the CMS and posted online, enabling consumers to compare patient satisfaction metrics from a variety of healthcare facilities. Patient behavior data describes how patients engage with care providers and their environments in the health care setting.

How is Data Analytics Used in Health Care?

Healthcare data analytics is a rapidly changing area of technology with new applications appearing regularly. Here are just a handful of current applications for healthcare data analytics:

  • Enabling faster drug discovery and development
  • Optimizing clinical trials and improving their reliability
  • Developing new healthcare interventions for target patient populations
  • Improving drug delivery by understanding patient behavior
  • Detecting disease outbreaks in a hospital or healthcare facility
  • Post-market risk management for medical device companies
  • Evaluate and compare patient engagement between hospitals to inform consumer choice
  • Supporting data-driven healthcare decision-making at all levels
  • Improving profitability in healthcare systems by evaluating marketing and sales performance and identifying new market opportunities

TigerConnect Leverages Healthcare Data Analytics to Optimize Patient Engagement

TigerConnect offers robust solutions for clinical communications, enabling two-way collaborative health conversations between patients and their care providers that drive customer engagement and satisfaction. Using TigerConnect Analytics, healthcare organizations develop insights into their adoption and usage of the application that can be used to drive patient engagement initiatives and boost HCAHPS scores.

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About TigerConnect

TigerConnect is healthcare’s most widely adopted communication platform – uniquely modernizing care collaboration among doctors, nurses, patients, and care teams. TigerConnect is the only solution that combines a consumer-like user experience for text, video, and voice communication with serious security, privacy, and clinical workflow requirements that today’s healthcare organizations demand. TigerConnect accelerates productivity, reduces costs, and improves patient outcomes.

Trusted by more than 6,000 healthcare organizations, TigerConnect maintains 99.99% verifiable uptime and processes more than 10 million messages each day. To learn more about TigerConnect visit www.tigerconnect.com.