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.
A case study for employers and health plans
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 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 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:
Analysis clearly showed the value of preventive cancer screenings for members and for the state agency:
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.
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.)
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
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.
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:
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.
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.
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.
Through their collaboration with HDMS, Lowe’s is now able to:
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.
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.
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.
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:
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.
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.
Through their collaboration with HDMS, the insurer is now able to:
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.
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.
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:
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.
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
“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.”
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.”
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.”
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.
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.
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.
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 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.
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.
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.
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.