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Case Studies

Analyzing the Effectiveness of Third-Party Diabetic Management Solutions

Read how HDMS helped a Client determine whether they should renew their contract with their Diabetes Management point solution.

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Case Studies

Improving Employee Mental Health Solutions through the Power of Data Analytics

Read how a national retail employer partnered with HDMS to better understand the mental health needs of their population and how it impacted their productivity.

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Case Studies

Maximizing the Potential of Healthcare Data for Better Population Targeting

Read how Lowe's works with HDMS to understand trends within its population and  identify patterns that may indicate the need for proactive
intervention to improve its population’s overall health.

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Case Studies

Measure the Impact of Preventative Cancer Screenings with Patient Outcome Analytics

A case study for employers and health plans

The Need

The Affordable Care Act (ACA) requires employers to fully cover preventive screenings for breast, cervical/uterine and colorectal cancers.

For one state agency, declining member utilization of these preventive screenings was a cause for concern. Why were utilization rates dropping? Moreover, what impact was the reduction having on the agency’s costs and its members’ health outcomes?

The Analytic Challenge

The state agency, which administers health benefits for 205,000 employees and dependents, set out to identify the cost and outcomes of the ACA-required preventive cancer screenings. What the agency really wanted to know was whether the screenings were resulting in earlier cancer detection, which in turn required less invasive and less costly treatment.

For quite some time, the agency simply assumed that the screenings were cost effective. The challenge was to accurately quantify their impact at a time when:

  • The American Cancer Society (ACS) released new, more targeted guidelines that lowered the number of people it recommended for the preventive screenings.1 (The ACS believed the change would result in higher prevention rates even with fewer people screened.)
  • Screening utilization was declining.
  • Only 6 to 8 percent of members who were screened were actually diagnosed with cancer or a related condition as a result.

The Solution

The state agency’s population health manager (PHM) uses HDMS’ analytics and reporting solution on a quarterly basis to analyze trends in cost and utilization of employee benefits. With HDMS’ data management expertise, the PHM trusted the credibility of the analysis. To further evaluate the cancer screenings, the PHM took advantage of the solution’s built-in evidence-based guidelines to create episode-based analysis groups (cohorts) from claims and enrollment data to measure whether members:

  • Were diagnosed with any cancer within the three years prior to being diagnosed with breast, cervical, uterine or colorectal cancer. (This helped to identify new cancer cases as opposed to recurring cancer cases.)
  • Received medical services for a cancer diagnosis within 60 days of a preventive cancer screening.

The Results

Analysis clearly showed the value of preventive cancer screenings for members and for the state agency:

  • The majority of new cases of breast, colorectal and cervical cancer among the agency’s members were initially diagnosed as a result of preventive screenings.
    • 80% of new cases of breast cancer were associated with preventive screenings¹
    • 11% of members who received screenings received additional treatments – not just for cancer
    • Cervical cancer screenings led many members to additional uterine or ovarian testing
  • Members diagnosed with breast, cervical, uterine or colorectal cancer through the preventive screenings experienced fewer medical complications, as shown through lower relative health risk scores.
    • Breast Cancer
      • 00 Average risk score of members diagnosed with breast cancer
      • 88-6.53 Average risk score of members diagnosed with breast cancer
    • Cervical Cancer
      • 00 Average risk score of members diagnosed with breast cancer
      • 31-4.22 Average risk score of members diagnosed with breast cancer
    • Those diagnosed through preventive screenings recorded lower total costs of cancer care on a risk-adjusted cost basis, as well as relative to expected cancer treatment costs.
      • 9% Decrease in the cost of treatment for breast cancer
      • 6% Decrease in the cost of treatment for colon cancer
    • Overall, paid claims for all three types of cancer screenings was 3.6 percent lower than in previous years.

Data-informed insight improves health

Today, the state agency reviews a preventive screening dashboard every quarter to monitor outcome metrics. Furthermore, working together with HDMS to perform proactive data analysis may open up new insights into opportunities to reduce costs and improve member health. It’s just one powerful illustration of how robust data analysis can help employers and health plans measure and enhance the effectiveness of preventive health benefits.

In the Know

The ACS’ updated preventive screening guidelines are now focused on smaller populations. However, they target age and gender groups that account for 82 to 92 percent of breast, cervical, uterine and colorectal cancer diagnoses. Screenings identify 68 percent of new breast cancer cases and more than 89 percent of other new cancer cases earlier. So, although the number of eligible members who received preventive cancer screenings declined, compliance with Healthcare Effectiveness Data and Information Set (HEDIS) guidelines, which measures individual clinical care influenced by health plan programs, generally improved. (The exception was compliance for breast cancer screenings.)

 

Cancer Screening Case Study

 

¹Grady, D., “American Cancer Society, in a Shift, Recommends Fewer Mammograms,” The New York Times, Oct. 20, 2015, https://hms.harvard.edu/news/american-cancer-society-shift-recommends-fewer-mammograms

HDMS proprietary data

Case Studies

Population Health Analytics Strategy Reveals Opportunities for Improvement

Background

South Country Health Alliance (SCHA) is a county-based health plan serving twelve rural Minnesota counties. Formed in 2001, SCHA offers seven programs to its more than 41,000 members, all of whom are Medicare and Medicaid participants. SCHA’s mission is to empower and engage its members to be as healthy as they can be, build connections with local agencies and providers who deliver quality services, and be an accountable partner to the counties they serve.

The Need

SCHA’s overall objectives were to improve key performance indicators for its population health and care management programs, and better control costs while improving quality and care coordination. SCHA leadership identified four primary obstacles to achieving these objectives:

  1. Inability to aggregate comprehensive data from multiple community partners, all of whom used different data types, formats, schedules and rules for utilization.
  2. Lack of an analytical tool that would enable greater utilization transparency and help address growing cost containment pressures.
  3. Poor overall population management due to health care disparities in rural communities across the twelve-county service area.
  4. Health care delivery was focused primarily on more expensive reactive treatment versus less costly preventative care.

The Analytic Challenge

In order to address these challenges, SCHA needed to answer key questions about its member population; for example: Where are costs being generated? What are the trends in medical and prescription drug utilization? Are there anomalies in care plans, products, age groups or service categories that require management action?

Access to the comprehensive, actionable data required to answer these types of questions was difficult to obtain for two reasons. First, numerous community partners across SCHA’s geographically dispersed service area used disparate systems and datasets, so data intake was a challenge. Second, data was delivered to SCHA in multiple formats using varying monthly schedules and inconsistent rules for utilization, making the data difficult to analyze, consume and interpret.

The Solution

Working closely with HDMS, SCHA’s first step was to build an enterprise data warehouse to facilitate data gathering from disparate source systems across the twelve-county service area. This enabled data intake to be standardized, allowed for more efficient data aggregation and storage, and provisioned the stored data for analysis and reporting.

The second step was to unlock the value of the warehoused data through an HDMS solution that enriched the data, enabled robust analysis through various lenses, and supported reporting of data in a consumable format to aid interpretation and decision making. The HDMS solution addressed SCHA’s functional needs, as well as ensured ease of-use to internal staff and their community partners.

The Results

  • Initial cost savings: SCHA and HDMS worked together to expedite implementation of the HDMS solution by approximately 15 percent or 37 days. As a result, project costs were reduced by approximately $300,000, allowing SCHA to redirect these resources to support its mission and operations.
  • Enhanced analytical insight: Using data gathered from various feeder systems and enriched through the HDMS solution, SCHA is now able to conduct in-depth analysis into key areas of its population: uncovering where gaps in care are most prevalent, tracking utilization and cost trends by program and service type, identifying service type anomalies, and more.

For example, the population can be segmented into county-by-county analytical cohorts — such as episode treatment groups (ETGs) — enabling SCHA to identify opportunities for greater efficiencies in cost or utilization. As a result, care management programs are more effective, costs are better controlled, disparities are reduced, and overall population health improved. 

  • Ease and consistency of reporting: Analytical reports are produced in easy-to-consume formats that vary based on the needs of the audience while preserving patient confidentiality. Community partners in each of the twelve counties are now evaluated using common metrics. Best practices of those with favorable performance characteristics are shared with partners in other counties to improve overall quality and population health.
  • Improved decision making: Because of the ease with which a variety of reports can be generated, SCHA senior leadership is able to review operations more frequently and with greater consistency. Performance metrics that are outside a specified tolerance zone or trending unfavorably are quickly identified and rapidly addressed through additional management assistance and attention.
  •  Long-term value: SCHA is now able to transition its health care resources from reactive treatment to more effective and cost-saving preventative care, promoting greater value and member health improvement, and enabling SCHA to better fulfill their mission.

 

View the Case Study

Case Studies

Population Health Management Analytics: Better Population Targeting with Healthcare Data

Client Profile: Lowe’s

  • Location: Mooresville, North Carolina
  • Industry: Retail
  • Number of employees: 265,000

Key Program Highlights

Through their collaboration with HDMS, Lowe’s is now able to:

  • Understand trends within a population
  • Create patient-oriented, pro-active health programs
  • Shift focus from treatment to wellness
  • Emphasize more cost-effective health choices
  • Communicate and incentivize healthy lifestyle choices
  • Collect data in a safe & secure way
  • Analyze biometric, medical and pharmacy data
  • Compare Lowe’s population to national health benchmarks

Lowe’s has been working with HDMS since 2006 and the relationship continues to grow and expand. Lowe’s relies heavily on its health data to understand trends within its population and also to identify any patterns that may indicate the need for proactive intervention to improve its population’s overall health. The strength of the relationship allows HDMS to work as an extension of the Lowe’s benefits organization.

The Situation

Starting in 2011, Lowe’s began to make its health programs and services more patient-focused and proactive. To gain visibility into its population, Lowe’s knew that it would have to rely even more heavily on its health data to successfully shift from a focus on treatment to prevention and wellness. Such a transition, if successful, would further the company’s commitment to patient-centered care while emphasizing more cost-effective health choices.

Lowe’s was looking to achieve the Triple Aim of health care – better health outcomes, lower costs, and a better patient experience – by focusing on the programs and services most likely to help maintain and improve the health of its employee population. Only by garnering clinical, risk, financial, and wellness data would Lowe’s be able to effectively communicate its new health and wellness strategy to its population.

The Need

Lowe’s needed to find ways to more effectively craft and target messages about its health and wellness programs to different segments of its population. By doing so, Lowe’s would be able to emphasize its programs that promoted better health while reducing overall health costs – all based on the support of strong data. Emphasizing health and wellness while reducing costs would help Lowe’s get closer to its goal of achieving the Triple Aim.

Not only did Lowe’s need to learn more about the health of its population, but it also needed to find ways to communicate and incentivize healthy lifestyles to its entire workforce. How to do this most effectively lay hidden in the company’s health data. HDMS helped unlock the answers.

The Solution

Using wellness data gathered from members logging onto the Lowe’s employee health portal, Lowe’s and HDMS were able to analyze the total employee population. This method allowed the needed data to be collected in a safe and secure way. A key benefit of using log-in data was the natural segmentation of the population into different groups by frequency of use. The data was then analyzed in multiple ways through the customized data analytics platform for Lowe’s.

The Lowe’s employee health portal log-in data was separated and mapped into six unique cohorts for further analysis among the total population:

  • All Non-users: employees whose IDs were not captured by the portal.
  • All Users: employees who had logged in regardless of frequency.
  • Situational Users: employees who logged in 1-3 times.
  • Novice Users: employees who logged in 4-11 times.
  • Active Users: employees who logged in 12-49 times.
  • Super Users: employees who logged in 50+ times.

HDMS analyzed biometric, medical, and pharmacy data. Lowe’s and HDMS then compared the population to national standards by  measuring a specific plan’s performance against recognized benchmarks and national standards.

The Results

By separating out and analyzing each of the cohorts – both individually and against the other groups – Lowe’s was able to discover much more than they originally anticipated about the health of its employee population. Specifically, the data revealed that there was a correlation between the frequency of employee logins and an increase in risk factors for chronic diseases. By using this data, Lowe’s was able to target messages to the high frequency users that would emphasize prevention, health and wellness to the segment of its population that most needed those messages reinforced.

When looking at the correlation between demographics and risk, the data revealed that the average age and risk of each cohort population was comparable until the log-in frequency increased, starting with Novice users. The data revealed that users who log-in more frequently tend to show a different pattern of health services utilization – including preventative health screenings such as for colon, breast and cervical cancer. Among Novice, Active and Super users there was also a noticeable difference in age as well as in both prospective and retrospective risk. HDMS also analyzed variances in BMI among the populations.

By discovering a possible correlation between certain health indicators and frequency of log-ins to the member portal allowed Lowe’s to create targeted messages about the weight loss programs it offers, prevention strategies, healthy diet and exercise advice, and information about heart disease, diabetes and other diseases customized to address those specific health characteristics.

The data revealed overall population health – and shed light on where and how the company could develop and share messages with certain cohorts of its population that will help improve overall health while controlling costs. Additionally, the data gave Lowe’s clear insight into both how to communicate with its population and what wellness messages would resonate with different cohorts within its population.

 

Lowe’s Case Study

Case Studies

Custom Healthcare Payer Data Analytics Solution Improves Analysis of Health Management Data

Client Profile

  • Location: Southeast
  • Industry: Health Insurance
  • Number of covered lives: + 1,000,000

Key Program Highlights

Through their collaboration with HDMS, the insurer is now able to:

  • Unite market and product data in one platform
  • Standardize reporting processes
  • Produce reports relevant to diverse audiences
  • Enhance visibility into datasets
  • Complete an accurate analysis of program costs
  • Demonstrate value of health management programs
  • Save users valuable time and resources from manual reporting

As the number of programs designed to promote health and wellness continues to grow, so does the need to collect, normalize and analyze increasing volumes of health management data. This was certainly the case for a large Southeastern health insurance company, representing nearly one million participants.

Like other health care organizations, clinical reporting at the organization had become more complex and detailed over time. The lack of a cohesive and uniform platform paired with the growing need to integrate health management program data with other types of clinical and cost/use information increased demands on already limited resources. The use of multiple reporting tools across different business units also resulted in frequent data reconciliation issues, making accurate reporting a costly and time-consuming endeavor.

In addition, the insurer needed an effective method for evaluating the costs of the health management programs it offered to members. Together, the insurer and HDMS established a plan for leveraging current analytic solutions to gain additional insights, better meet the needs of its employer clients and address an increasing complex set of reporting needs.

The Situation

As a longstanding HDMS customer, the insurer successfully used HDMS’s flagship data analysis and reporting tool for more than 10 years. As part of the expanded collaboration, the insurer worked with HDMS to fully integrate the data from several ancillary services into the tool. The organization also expanded its collaboration with HDMS to include Population Health Management analytics which deliver presentation-ready management reports for its employer groups.

To build on these investments, enhance visibility into its datasets, better evaluate costs and demonstrate program value, the next step was to implement a more comprehensive – yet flexible – way to analyze and review health management program data. By placing data into one unified platform, the insurer sought to increase efficiencies, save valuable staff time and preserve resources that could be devoted to other mission-critical tasks.

The Need

At the outset of the project, the insurer identified a number of specific needs and requirements the new clinical health management reporting platform would need to meet, such as the ability to input new data from diverse sources and apply standardized codes and formats. The platform would also need to allow intuitive, easy access for users, including case managers, company leadership, health managers and account managers.

Recognizing the development of such a solution would require tight collaboration and flexibility between both organizations, HDMS worked closely with the insurer to meet these needs by:

  • Incorporating new data sources as they were identified
  • Establishing a uniform, underlying file architecture
  • Formulating diverse enrollment, engagement terminology and access codes between health management, case management and complex care
  • Reaching consensus on data methodology and reporting objectives

The Solution

The collaboration between HDMS and the insurer resulted in a highly flexible, customized and user-friendly system. Building on the existing data analytics platform, the new solution allowed data input from many more sources – including disease management, case management, lifestyle management and wellness program data – all of which were vital to assess the effectiveness and utilization of health management programs.

The new platform also unified codes, standardized processes and provided customized templates, tables and dimensions that took the needs of all health data end users into consideration.

Ultimately, more than 30 customized clinical eligibility dimensions – a collection of reference information used to determine whether a member may or may not be considered to have a condition or be allowed to enter a care management program – were added to the platform to facilitate greater data analysis and reporting. These included both participant and risk dimensions as well as customized data tables for a range of wellness-specific initiatives.

The Results

As a result of this collaboration, HDMS’ customized reporting solution has delivered a wealth of benefits for members and staff. One of the biggest advantages so far has been the ability to combine data, analysis and reporting in one platform. In conjunction with the predictive modeling tool, users can quickly and easily analyze enrollment, participation and cost data for a wide range of health management programs.

“The biggest value from the HDMS platform is the customization of the data base based on customer’s specific needs. The  platform allows us to send and pull data independently of other areas. Now we can add indicators to HCC reports to identify participants and see if they’re in disease management, case management or maternity management.” – Medical Director

Additional benefits include:

Reduced wait times for reports

“Without the need to aggregate multiple platforms, reports that once took staff all day to produce can now be generated in as little as an hour. Beyond dramatically accelerating report delivery times to clients and staff, these new efficiencies have freed up valuable resources that can now be devoted to other projects and initiatives.”

Higher Client Satisfaction

Whether clients want to better understand what’s happening related to admissions, out-of-network claims or emergency department (ED) visits, users are empowered to quickly and efficiently produce customized, high cost claimant reports, and conduct drill down analysis by claimant, facility type or service.

 “The new reports and deliverables have been very well-received by clients. In fact, with the level of customization and detail the reports now provide, we have secured a competitive edge in the local marketplace.”

 Improved Ease of Use

While previous HCC reporting processes required a great deal of manual intervention, the new platform dramatically streamlines workflow and eliminates the need for the many hands-on, cumbersome steps that caused a drain on productivity. Clinical teams find the participation-to-utilization linkage to be especially useful.

 “Health management data is now better organized and presented in a more intuitive format.”

 “Our clinical leaders and staff, including health coaches, appreciate the fact they don’t need to have a programming background to capture the information and reports they need.”

 Enhanced decision support

The combination of timelier reporting and episode data enable more accurate recommendations for program and benefits plan design going forward. For example, predictive modeling information, such as risk scores, also enables nurses to prioritize outreach efforts. Using this information, the organization was able to increase program enrollment by more than 650% in the span of two quarters.

Increased data visibility

The platform allows users to identify HCCs and see how risk levels shift and change over time based on a variety of factors like age or recent diagnoses. For opt-in programs like maternity management, having the ability to rapidly identify members that could benefit from these programs but are not yet enrolled can help drive more effective outreach and engagement.

 

HDMS Health Plan Case Study

Case Studies

Reducing Readmissions from Skilled Nursing Facilities in the Era of Value Based Care

Background

In an effort to improve health care quality, safety, and outcomes, a large Midwestern health system representing 720,000 attributed members (including commercial, Medicare, and Medicaid populations) collaborated with Health Data & Management Solutions, Inc. (HDMS) to examine transition of care (TOC) data. This assessment examined discharges from skilled nursing facilities (SNF) that resulted in readmission to an acute care hospital within 30 days.

The Need

Hospitalizations associated with long-term care residents can be expensive and lead to negative outcomes for individuals in skilled nursing facilities, especially for the elderly and people with disabilities. Research has shown nearly a fourth of SNF stays result in a hospital readmission within 30 days of the initial admission, costing an average of $10,000 per hospitalization.¹

To provide residents with better care experiences and outcomes, the health system sought to assess all of its SNF stays taking place between the first acute and second acute stay. This measurement would provide the health system’s clinical leaders with a more complete picture of the number of admissions from or into a SNF. In addition, this patient-level data would provide the health system with actionable insights for advancing their quality initiatives and improving care, as well as identify preferred facilities based on their success in keeping patients from returning to the hospital.

The Analytic Challenge

The challenge the health system faced was linking several clinical events at the patient level and across time. To appropriately assess TOC data, the health system needed to identify all SNF stays, including transfers and multiple admissions. Using this information, they pinpointed the initial acute care hospitalization and determined if there was a subsequent readmission to an acute care facility within 30 days of the discharge date of the initial hospitalization. Further analysis required readmission rates—defined by the number of readmissions to acute care facilities divided by total SNF stays—to be broken out by patient type, facility, etc., to develop and drive quality improvement programs.

The Solution

Working closely with the health system, HDMS created new metrics to account for and identify all SNF transfers. These new metrics counted readmissions starting with the initial acute admission, SNF stay, and subsequent acute readmission within 30 days of initial discharge. HDMS’ flexibility in identifying all necessary SNF transfers data provided the health system with a comprehensive view of acute readmissions. The adjusted logic—including a SNF transfer in between admissions—resulted in more meaningful data to support the measurement and analysis of SNF quality and performance.

The Results

Upon implementation of the metrics, the health system was able to identify key insights that allowed for actions to reduce readmission rates. By expanding the analytic parameters for SNF readmission measure, the health system could more accurately assess readmissions, trends and SNF quality.

For example, the health plan uncovered overlooked readmissions that were not factored into the overall readmission rate. This data enabled the health plan to determine the true overall readmission rate, which was higher than previously understood. Armed with this new insight, the health system was able to better align resources and reduce its list of SNFs from approximately 100 facilities to a preferred set of 41 facilities with the best performance. This initiative increased the quality of care for the health system’s patients while reducing readmissions to acute care facilities and costs. The new SNF readmission measure has also been used in profiling quality across more than 400 physician groups within the health system’s network—resulting in similar positive changes being observed across the entire network.

 

Provider Case Study

 

¹Mor, V., Intrator, O., Feng, Z., et al.: The revolving door of rehospitalization from skilled nursing facilities. Health Af. (Millwood) 29(1):57-64, Jan.-Feb. 2010.