Clinical Analytics Improves Health Care—6 Facts You Should Know

INTRODUCTION

The clinical data analytics industry is poised to expand by more than $18,700,000,000 through the year 2020.  The 4 areas of analysis predicted to be in highest demand are financial analysis, supply chain analysis, fraud & human resources, and, of course, clinical.

In fact, some experts say that there’s never been a better time to implement clinical analytics, considering how badly healthcare reform is needed.  If reducing overall cost and improving the quality of care are indeed the goals of reform, then technology such as clinical data analytics must be viewed as our primary means of achieving such lofty goals.

WHAT IS CLINICAL ANALYTICS?

Clinical analytics is the process of using informational analysis to draw meaningful conclusions using the data collected from the different segments of healthcare:  pharmaceutical research and development (R&D), claims & cost, electronic medical records (EHRs), and patient sentiment & behavior (from surveys, case studies, purchasing preferences/practices, etc.).

Conclusions and discoveries may then be used to guide decision-making regarding changes that need to be made, new ways of doing things, and the establishment of “best practices” criteria.

Clinical analytics can illustrate ways to improve health care, while at the same time reducing cost by analyzing trends, patterns, and deviations from the norm revealed by the data.

THE 6 IMPORTANT FACTS YOU SHOULD KNOW

                                               

1.Differing in terms of the types of information users, data involved, and the management decisions or clinical action supported by the analysis, there are approximately 8 different types of clinical analytics—these include:

  • Public Health Surveillance
  • Comparative Effectiveness
  • Quality Improvement
  • Clinical Decision Support
  • Clinical Benchmarking
  • Population Health Management
  • Regulatory Reporting, Compliance & Transparency
  • Retrospective Performance Measures & Predictive Analytics

2. Clinical analytics works either on a prospective (zooming forward) or a retrospective (zooming into the past) perspective. Performance measuring tools/systems using data about patients gathered over periods of time is an example of “retrospective.” Such information can be used to ameliorate a population’s health or an organization’s performance.

CDS (clinical decision support) systems, on the other hand, are predictive or prospective since they help guide future action (e.g., treatment plans) based on past information about individual patients.

3. There are 5 main types of performance measuring tools:

  • Process Measures:  compliance levels for standard-of-care programs (e.g., requiring mammograms for certain women)
  • Cost, Resource & Efficiency:  treatments/tests used (e.g., MRI to confirm CT scan’s vague results), outcomes, etc.
  • Structural Measures:  how healthcare delivery systems operate (e.g., number of prescriptions sent electronically)
  • Patient Experience:  patient satisfaction/perceptions (mostly through surveys)
  • Outcomes Measures:  impact on patient health of healthcare delivery (e.g., mortality rates) and patient safety (e.g., nosocomial infections)

4. Clinical analytics can greatly improve the quality and reduce the costs of healthcare delivery—such as by:

  • Promoting/encouraging prevention-based (rather than “reactive”) healthcare—a practical, cost-saving solution;
  • Promoting/encouraging evidence-based treatment plans—using patient histories to guide treatment plans;
  • Enabling more personalized patient care—reducing test repetition & change-of-doctor-created lack of coordination;
  • Improving diagnostic accuracy—using predictive algorithms to make patient decisions;
  • Providing hospitals and employers with predictions about insurances products costs.

5. Clinical analytics improves health care–this key role in improving healthcare and R&D systems is partly made possible thanks to the “Center for Medicare & Medicaid EHR Incentive Programs.” Using data from electronic health records, healthcare providers and researchers, thanks to this major initiative by the Center for Medicare and Medicaid Services (CMS, can start making use of a vast amount of data which would otherwise remain poorly utilized.

6. Clinical analytics improves healthcare by providing the following benefits:

  • Improving margins
  • Reducing waste
  • Streamlining operations
  • Improving performance
  • Automatically tracking quality measures
  • Shared-savings arrangements success
  • Complete cost-structure understanding
CONCLUSION

Clinical analytics may sound like a fancy term reserved only for IT and management personnel, but, at the core of this technology is the basic fact that the huge amount of data being collected every day about millions of patients can be used as an effective tool to fix, transform and reform the broken healthcare system.

Practically speaking, this means reducing costs across the board, improving outcomes and enhancing the quality of care for all patients.

Although some healthcare reform ideas unfairly favor one stakeholder or segment, clinical analytics, if applied objectively and responsibly, can be a powerful tool for meaningful, long-lasting, helping-all-involved and proactive change.

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