Analytics

The global healthcare/medical analytics market is expected to reach USD 53.65 billion by 2025, according to a new report by Grand View Research, Inc. Increasing need to reduce healthcare expenditure among hospitals, and other healthcare providers is anticipated to boost growth in the market.

  • The digitalization of healthcare data is also one of the primary drivers of healthcare analytics. According to Intel, approximately more than 80.0% of the healthcare organizations in the U.S. have adopted Electronic Medical Records (EMR) systems. These systems collect a lot of data, which can be analyzed using various types of healthcare analytics to develop personalized medicine.

Challenges of Big Data Analytics in Healthcare

  • Capture: Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is an ongoing battle for organizations, many of which aren’t on the winning side of the conflict.
  • Cleaning: Dirty data can quickly derail a big data analytics project, especially when bringing together disparate data sources that may record clinical or operational elements in slightly different formats. Data cleaning – also known as cleansing or scrubbing – ensures that datasets are accurate, correct, consistent, relevant, and not corrupted in any way.
  • Storage: Cloud storage is becoming an increasingly popular option as costs drop and reliability grows. Close to 90 percent of healthcare organizations are using some sort of cloud-based health IT infrastructure, including storage and applications according to a 2016 survey.
  • Security: Healthcare organizations must frequently remind their staff members of the critical nature of data security protocols and consistently review who has access to high-value data assets to prevent malicious parties from causing damage.
  • Stewardship: Understanding when the data was created, by whom, and for what purpose – as well as who has previously used the data, why, how, and when – is important for researchers and data analysts.
  • Querying: Many organizations use Structured Query Language (SQL) to dive into large datasets and relational databases, but it is only effective when a user can first trust the accuracy, completeness, and standardization of the data at hand.
  • Reporting: The accuracy and integrity of the data has a critical downstream impact on the accuracy and reliability of the report. Poor data at the outset will produce suspect reports at the end of the process.
  • Visualization: At the point of care, a clean and engaging data visualization can make it much easier for a clinician to absorb information and use it appropriately.
  • Updating: Healthcare data is not static, and most elements will require relatively frequent updates in order to remain current and relevant.
  • Sharing: Data interoperability is a perennial concern for organizations of all types, sizes, and positions along the data maturity spectrum.

Trends in Healthcare Analytics

  • Healthcare leaders are just beginning to understand the potential for healthcare analytics to transform decision making through use of predictive algorithms and accurate forecasts. There is little doubt that other industries have used the power of predictive analytics to power strategic growth and margin enhancement, and that healthcare needs to keep up. How should healthcare leaders prepare to leverage this potential?
  • Recognize that your organization’s analytics capability is a strategic asset, and develop a plan to enhance your analytics assets, including their people, processes and technology. Comprehensive assessment of the organization’s current analytics capability is a complex undertaking, and obtaining expert assistance from an external resource to establish baseline performance and create a roadmap for enhancing value is a wise investment.
  • Assess baseline performance for critical enterprise capacity functions. Capacity equates to currency, and patient flow is the leading driver of margin. Is your current capacity plan aligned with your strategy for profitable growth? How well are your daily capacity management and staffing plans aligned with that strategy? For instance, how many hours are patients held in ED, PACU, or ICU while waiting for a bed at the appropriate level of care? In a recently conducted analysis, we discovered a $9.3 million opportunity for a hospital with an average daily census of 400. Applying predictive analytics to questions such as these separates top performers from the pack.
  • Invest in the talent needed to develop a healthcare analytics program that concurrently illuminates the drivers of current performance gaps, provides real-time support for clinical and operational decisions, and drives development of predictive algorithms and accurate forecasting.