Enabling High-Value Provider Performance Through Tailored Healthcare Analytics

With roughly 30% of all health spend being of low-value, it’s a trillion-dollar problem and growing.

HealthCorum Dashboa
HealthCorum Dashboa

Thought Leadership Article:

Data-Driven Insights Key to Advance Value-Based Care 


Identifying Wasteful Spending

Benchmark and score providers relative to their peers using cost, quality, and efficiency metrics 

HealthCorum has developed proprietary technology and methodologies to analyze various healthcare data, including

Detection of low-value care spending using Artificial Intelligence (AI)
Identification and optimization of referral activity between providers
Recognition of potential conflicts of interest by linking prescribing patters to pharma company payments received
Analysis of provider network leakage
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Create New Networks

Expedite the research process and be better prepared for contracting discussions

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Optimize Existing Networks

Compare providers to one another in a fair and meaningful way in order to assess areas that need improvement

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Expand Existing Networks

View data on all available providers, including proprietary scores that indicate the relative cost efficiency of each provider

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Educate Providers

Share data-driven benchmarks to facilitate conversations with providers and improve performance

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Gain Competitive Advantage

Opportunities to execute various cost-saving strategies

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Quantify Low-Value Care

Instantly analyze claims data to identify low-value care with our AI-driven process

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Detect And Analyze Referral Patterns

Surface provider referral patterns and work to eliminate inefficiencies 

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Surface Competitive Intelligence

Aggregate data on all providers in each selected network to deliver a greater understanding of network strengths and weaknesses

Markets we serve



Self-Funded Employers
Provider Groups
State Agencies + Collaboratives

Low-value care detection

Tracking and quantifying low-value care services has been difficult and labor intensive -until now. HealthCorum has developed a first-of-its kind AI technology called the HealthCorum Neural Network (HNN) which learns from existing clinical guidelines and formulates an unbiased logic to recognize instances of low-value care in health claims data. This proprietary breakthrough allows us to quickly analyze millions of lines of data with fewer resources and greater accuracy than existing methods.

Referral pattern identification and optimization

Referrals are an important part of tracking patient flow and communication between primary care and specialty providers, yet these relationships are not always apparent. Physicians decide to refer patients to other physicians for many reasons, ranging from the need for specialization to addressing problems of overcrowding. A physician’s decision to refer (or not refer) a patient is essential in determining cost and quality of care. We have developed an algorithm based on preferential attachment methodology that can analyze claims data and identify referral patterns.

Potential conflict of interest recognition

Every year pharmaceutical companies pay doctors billions of dollars for consulting, promotional talks, and meals. But are these payments creating a conflict of interest? Our analysis finds doctors who received payments from pharmaceutical companies linked to specific drugs, and then follows their prescribing patterns to determine if an abnormal amount of those specific drugs are being prescribed to their patients.

Network leakage analysis

For payors and health systems, it is crucial that patients stay in-network or visit preferred providers as often as possible. Patients may visit out-of-network providers for a host of reasons – many of which can be avoided with the right information. HealthCorum leverages referral identification technology to deliver insights into patient journeys and report on individual physician capture rates, primary care to specialist leakage, and overall network integrity.

Take complexity out of provider network optimization with a holistic view of provider efficiency and value-driven referral patterns.

We work collaboratively to accomplish your goals, achieve better outcomes and reduce the delivery of low-value care.

What is HealthCorum's definition of a 'low-value' care (treatment)?
HealthCorum process identifies a treatment as low-value if one of the conditions below is true:

A. The treatment is medically unnecessary.
B. The benefits are short-term.
C. A lower-cost alternative is available.

Can HealthCorum use our internal data?

Absolutely! HealthCorum platform is equipped to analyze claims and create scores based on client data. There are some basic guidelines and minimal required fields that any dataset should contain. HealthCorum’s scores are designed to harness the richness and homogeneity of CMS data covering significant portions of the physician’s patient’s panel. However, we understand the value and fit-for-purpose use of our partners’ own data, which is reflective of the physician patterns through the lens of their own populations. While CMS-based scores establish a good foundation for understanding physician groups’ relative appropriateness for value-based arrangements, we recommend fine-tuning interventions/strategies based on scores derived from your own population.

Many of our providers have multiple specialties. How does HealthCorum determine specialties for peer-to-peer comparison?
Our proprietary algorithms assign specialties/subspecialties to providers based on how they spend their time and the drugs they prescribe observed in the claims data. The algorithms further break up diverse specialties (such as orthopedics, internal medicine, and cardiology) into subspecialties. For example, HealthCorum compares orthopedic surgeons’ practice patterns who primarily perform hip surgeries to other hip surgeons in the same geographic area, serving a similar population. Internal medicine physicians with activity like nephrologists are classified as nephrology versus internal medicine providers who function as primary care.
How does HealthCorum account for regional variations?
HealthCorum uses Hospital Referral Regions (HRR) to define peer group geographies. The HRR is a geographic unit used by the Dartmouth Atlas for health care to define the hospital service area containing the referral hospital or hospitals most often used by residents of the region. CMS, HealthCorum, and many other organizations use this geographical framework to make comparisons.

Does HealthCorum use Personal Health Information (PHI)?
HealthCorum does not use or accept PHI data. A unique identifier provided in the dataset is used to build relational data.