The Healthcare Effectiveness Data and Information Set (HEDIS) is a tool used by more than 90 percent of American health plans to measure performance on important dimensions of care and service. HEDIS consists of 81 measures across five domains of care. The National Committee for Quality Assurance (NCQA) collects HEDIS data directly from health plan organizations and providers for multiple purposes via the Healthcare Organization Questionnaire (HOQ), and non-survey data through the Interactive Data Submission System (IDSS). Commercial HEDIS data is used to calculate national performance statistics and benchmarks, as well as to set standards for NCQA Accreditation. Commercial data is also included in the NCQA’s Quality Compass® tool. The NCQA also collects Medicare HEDIS data on behalf of the Centers for Medicare & Medicaid Services (CMS), and Medicaid HEDIS data on behalf of state agencies. In addition, NCQA collects commercial statistics on behalf of some states and the U.S. Office of Personnel Management for health plan report cards. The CMS requires all Medicare Advantage managed care plans to report HEDIS information. The Federal Employees Health Benefits program has also recently adopted HEDIS reporting initiatives, and numerous states that offer Medicaid managed care plans are also required to report HEDIS performance measures.

Health plans annually collect HEDIS statistics for reporting in the upcoming year. Analyzing this information makes it possible to compare the performance of health plans on a verifiable and credible basis. Medicare Advantage plans are given a yearly Star rating based on its performance on pre-defined HEDIS measures, with other various plans and marketplaces also providing ratings based on HEDIS measurements as well. These ratings are published in the open market A hypothetical impact of HEDIS on plan reimbursement is noted below: to help consumers pick plans that meet their requirements. A higher rating helps the organization earn bonus payments and rebates from the government. These ratings also directly impact an organization’s top-line revenue acquisition through a higher number of enrollments due to two reasons:

1. Higher rated plans are allowed to sell insurance throughout the year

2. Higher rated plans attract more new members due to their better visibility and reputation in the market.

Therefore, healthcare organizations must not only maintain plans with higher Star ratings, but also to improve any plans with ratings below four Stars.

A hypothetical impact of HEDIS on plan reimbursement is noted below:

Impact on Star Rating on Reimbursement


Focused efforts to improve CMS Star Rating and HCC RAF can have significant impact on a plan’s financial performance

Component Formula/Basis
2016
3.5 Stars 4.5 Stars, Improved Diagnosis Coding
 
Determining Variables
A 1.00 Benchmark Per CMS & County Mix $785.00 $824.25 --> Star Rating
B Risk Score Plan Population 0.935 0.95 --> HCC RAF
C Risk-adjusted Benchmark A*B $733.98 $783.04 ~$40M/year* in incremental revenue for every 100,000 members
D 1.00 Bid E/B $737.97 $726.32
E Risk-adjusted Bid Plan Estimate $690.00 $690.00
F MA Basic Premium Max{D-A,$0} $0.00 $0.00  
G Savings Max{C-E,$0} $43.98 $93.04  
H Rebate % ACA Rebate % 65.00% 70.00% -- > Star Rating
I Rebate G*H $28.58 $65.13 -- > Star Rating & HCC
-- > RAF
J Member Premium Affected by CMS revenue, benefits and plan profits $0.00 $0.00
K Total Revenue E-F+I+J $718.58 $755.13 Additionally,
  • Admin expense can be improved through process improvement and labor efficiencies
  • Medical expense can be lowered through improved outcomes and payment integrity
L Medical Claim Expense-Medicare Benefits Plan Estimate $596.85 $596.85
M Medical Claim Expense-Additional Benefits Plan Estimate $27.60 $27.60
N Medical Claim Expense - Total L+M $624.45 $624.45
O Administrative Expense 11%*K $79.04 $79.04
P Gain / (Loss) K-N-O $15.09 $51.64
Q Gain / (Loss) as % of Revenue P/K 2.10% 6.84%
*$40M = $36x12 months x 100,000 members

The importance of HEDIS abstraction

An important step in managing HEDIS data is coding and abstraction. Data abstraction is the review of medical charts to document whether NCQA quality of care guidelines have been met. In order to have credibility during an NCQA audit of HEDIs data, information from medical records must be verifiable and well documented. This effort is critical for managing the reporting, as well as for enhancing the quality of outcomes for plan participants and providers through identifying gaps in care.

Data abstraction is performed by obtaining copies of the records and abstracting them upon receipt, or by sending abstraction teams to provider offices. The types of records used for NCQA audits are documented through the organization’s accreditation websiteii . Healthcare organizations select records for the abstraction process using claims analytics programs that determine any missing elements for HEDIS metrics during a reporting year, such as no identified BMI, lack of mammogram or no immunization report. These records are input into a database where internal or third-party software programs initiate a retrieval process for these missing elements by contacting providers, then requesting copies of medical records for further review. The documents are often scanned, digitized, indexed, linked to displays or in some instances available on secure mobile devices for review. Abstractors conduct a detailed review of medical charts and insurance claims for hospitalizations, medical office visits and other procedures to determine if they meet the quality of care measures defined by the NCQA for the yearly HEDIS audit.

As data collection of performance metrics increases, so do opportunities for the automation of coding and abstraction services – especially those that expedite and systematize the laborious process of manual record reviews. For members and patients, attaining high HEDIS measure scores is directly linked to delivering costeffective care and better health outcomesiii. Improving HEDIS metrics can translate into millions of dollars in reimbursement from sponsored healthcare programs like Medicare and Medicaid.

Challenges in HEDIS abstraction

As health care leaders know, HEDIS abstraction is a highly manual and intensive process requiring significant reviewing and auditing of large volumes of medical record information. In addition, the gathered results do not readily facilitate closing gaps in care through member or provider communication. Unmanaged gaps in care result in non-compliance with NCQA standards, leading to lower Star ratings. Current HEDIS operations tend to focus on transactions, rather than closing these gaps. This can be caused by providers not responding or sending the correct medical records, as well as a lack of follow up process. High hold times of sub processes such as data abstraction, fax conversion and outreach can also further reduce productivity. Completing these processes manually can consume time better spent allocating, consolidating, and mapping data with the correct records. These are all logical operational areas for automation and machine learning. Applying automation principles can reduce the laborious and time-consuming abstraction process while improving reporting efficiencies, cost management and clinical outcomes. Common goals for this type of project focus on enhancing CMS client satisfaction and member outcomes, such as:

  • Improving care gap closure rates to boost Star ratings
  • Providing quality care to members
  • Increase revenue through rebates
  • Attain a leading market position by using abstraction services to increase health plan visibility to potential members

Case study

Challenge

An EXL client had a gap throughput of 6.7% for total number of gaps closed per 100 calls made. They also faced high average hold times at eight minutes for data abstraction, six minutes for fax conversion and outreach at ten minutes.

EXL solution

EXL applied automation and machine learning principles to decrease the amount of time spent waiting and improve gap closure rates.

Results

  • Boosted Star ratings due to closed gaps
  • Increased revenue through rebates
  • Gained a leadership in market position leading to higher health plan visibility

Identifying what issues to solve: Health plans and providers often identify several issues that require solutions:

  • Revenue optimization
    • + Reimbursement limitations, or penalties stemming from unmanaged gaps in underperforming plans
    • + Poor market position and new member acquisition due to low-quality care results
    • + Provider dissatisfaction relating to pay-for-performance or quality-based compensation
  • Clinical quality Improvement
    • + Low gap closure rates limit health status improvement
    • + Potential increase in ER and hospital utilization
    • + Missed opportunities in disease and case management

HEDIS oriented solutions to address these issues through leveraging HEDIS -based analytics to identify, document and prioritize closing gaps in care through:

  • Creating a hierarchy of revenue optimization opportunities by incorporating motivational indexing of members
  • Allow for provider segmentation and prioritization to facilitate member engagement
  • Provide ongoing metrics-based tools to automate and improve the efficiency of gap closure and abstraction

HEDIS based outcomes should address measure the improvements resulting from closed care gaps, as well as the how this impacts the overall quality of care.

Process and project design strategies

In order for plan and provider leadership to improve HEDIS management and processes, it is important to identify, quantify and manage opportunities to achieve a strong ROI. A separate EXL white paper addresses many opportunities for robotics process automation which can be applied to HEDIS management.

Using the right approach in HEDIS process improvements can result in a high performance capability. This approach is often focused on three operational areas:

  1. Outreach call to providers
  2. Fax Conversion
  3. Data Abstraction

Plans with fewer than four Stars can target these areas to close gaps in care and achieve positive outcomes. When undertaking such a project, the health plan leadership must consider the potential cost benefit of initiating a performance improvement program, such as the impact on the overall topline through additional rebates and bonuses provided by CMS. This information can be used to coordinate the resource commitment necessary for the project.

When health care organizations consider making performance improvements, it is important to strategize as creatively as possible. Whether done alone or in conjunction with a service provider, it is important to be as innovative as possible in constructing a solution, possibly using design thinking strategies. Such an approach requires a comprehensive, committed multidisciplinary team and open communication to construct a new solution.

Problem solving process and team structure

Health plans would be recommended to implement a structure to enable design enhancements. This framework should incorporate:

  • Value stream mapping for identifying non-value adds in the process and develop interventions for analytics, technology and practice
  • Six Sigma tools to establish process baseline and targets
  • Design Thinking-based customer journey mapping to identify member painpoints that should be addressed when redesigning the process, shifting the focus from a transactional level to one focusing on member care gaps

Plans and providers need to incorporate defined design principles to clearly articulate and solve problems such as improving inefficient HEDIS processes. These design thinking approaches are extensively reviewed in a separate EXL white paper.

When applying design improvements, it is critical to include broad based inputs into the proposed solution. For instance, the following types of input would be required for resolving clinical, IT and analytics issues:

Solution Description Type of Input
1. Provider contact strategy
1.1 Provider segmentation
1.2 Best time to call
1. Provider segmentation basis response, scale, measures rating, tech maturity, etc.
1.2 Best time to call + shift optimization proposal
Analytics
2. HEDIS G-A-P-S (Gap Allocation Prioritization System)
2.1 Provider allocation
2.2 Follow up process
2.3 Provider filter and case ownership
Gap tracking and closure from start till end - allocation, prioritization of work basis defined startegy Technology
3. Provider directory lookup Look up provider contact numbers through provider databases and store the correct (last contacted) numbers in local database Technology
4. Text mining / OCR for abstraction Improve efficiency and accuracy of abstracting medical record Robotics
5. Member services outreach Proposal to reach out to members for gaps closure Practice
6. Fax form redesign   Practice


Solution construction

A solution construct must not only meet any HEDIS abstraction issues, but also provide continuous, ongoing process improvement while offering an opportunity for broader market use and revenue acquisition. This could be done through an automated system that with built-in analytics models for provider segmentation, contact strategy, and gaps prioritization. Such a capability would improve HEDIS gap closures and ultimately achieve better Star ratings.

Technology is the foundation for automating continuous process improvement. This strategy requires robust solution that can manage the gap closure life cycle end-to-end through the following features:

  • Multiple file upload and duplicate removal to efficiently upload multiple sources of member information and seamlessly remove duplicate records.
  • Analytics-based, configurable workflows allowing for intelligent skill-based task routing. This enables allocating and prioritizing accounts based on record source, work type, gap measure weight and plan rating. The right records for the highest priority gaps in care can be allocated to the nurses with the right skill set. Such a prioritization model also helps reduce touch points for these cases.
  • Announcement and operational views that help supervisors cascade information related to process updates, as well as providing employees a view of their current and historical performance, work schedule and helpline numbers.
  • Real-time performance insights on members with gaps in care that inform nurses about their performance on abstracted records, converted fax and provider outreach. This information can also be used to provide gap closure rates for the leadership team.
  • Customizable operations business reports based on business needs
  • Real-time performance dashboards for leadership teams to monitor the rate of progress on gap closure rates.

Achieving continuous operational improvements and enhancements in HEDIS designs requires incorporating analytics functions that enhance clinical and financial outcomes. If automation is utilized for that end, it must integrate datadriven machine learning in the pursuit of process excellence. Both of these tasks require implementing various analytics capabilities:

  • Embedded impactful analytic insights in an end-to-end provider contact strategy to improve the overall gap closure rate
  • Segmenting providers, and identifying factors influencing the acquisition process to assist with collecting the complete medical records necessary to close gaps
  • Integrating historical data to identify the best time to reach different providers, thereby potentially reducing the number of attempts and increasing capacity for coding professionals to engage more providers
  • Prioritize account-based engagements using multiple variables such as the total number of members for each provider, care gaps of each member, location, response rate and plan rating

End to end management – Coordinating the internal organizational to apply automation principles

Closing HEDIS gaps requires clearly defined metrics that ensure consistent, accurate reporting and identify opportunities for improved coordination across the corporate matrix. A fragmented approach to contacting providers and requesting medical records, or lack of ownership and continuity between the voice processes and back office processes will result in fewer gap closures and low plan ratings. This can be avoided by using technology-based solutions powered by analytical models to manage case ownership and provide case prioritization, thereby building synergies between voice processes and back office processes.

Outcomes

A HEDIS automation improvement process must identify success metrics early in in the design process. Healthcare organizations should use reporting metrics that clearly show the operational and clinical return on the re-engineering investment. Verifying and clearly documenting these improvements is critical to the success of future programs and marketing opportunities.

Potential transferability for revenue acquisition

Healthcare companies are increasingly interested in partnering with innovative technology, service and analytics providers to develop HEDIS performance improvement capabilities due to the potential for transferable services and increased revenue acquisition. Standalone non-intrusive solutions built on easily customizable platforms can support revenue generating function for health plan organizations. Such a platform should contain the following features:

  • Embedded analytics that prioritizes gaps based on their weights and probability of closure
  • Provider segmentation and contact strategies for enhancing call success and gap closure rates
  • Built-in provider contact information lookup
  • An automated de-duping and allocation process
  • Real-time dashboard with data visualization to optimize decision making

In summary, improving HEDIS metrics is essential to enhancing reimbursements for plans and providers. In order to maximize top-line revenue generation, both operational efficiency and clinical outcomes must be simultaneously enhanced. This can be accomplished through the use of design thinking and the application of advanced automated programs.

References

  1. http://www.ncqa.org/hedis-qualitymeasurement/hedis-data-submission
  2. http://www.ncqa.org/portals/0/ hedisqm/hedisroadmap/section_4_ medical_record_review.doc
  3. http://info.carenational.com/blog/ whats-the-difference-between-hedisabstraction-hedis-over-reading

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