Advanced payment models incentivize Accountable Care Organizations (ACOs) to deliver high-quality care and close gaps in care for members, thereby earning shared savings and increasing profits. However, in order to succeed and identify gaps in care, ACOs must be able to rely on solid data and analytics to avoid losing income that could be invested back into patient care. Utilizing its analytics platform and a quality measures solution has allowed Hospital Sisters Health System to close care gaps, improve ACO quality measures performance, and enhance reporting accuracy and effectiveness.
agilon health immediately understood the sizeable risk COVID-19 presented its members. The organization leveraged data and analytics to quickly develop a COVID-19 mortality risk model. agilon health distributed the risk data to its market partners for outreach, improving the ability of its partners to intervene to improve individual and population health.
OneCare Vermont sought to identify which of its patients were at the highest risk of serious illness or mortality from COVID-19 and which of its patients with chronic healthcare problems needed care for medical issues other than COVID-19. Leveraging its data platform, OneCare Vermont enabled rapid identification of at-risk patients. Providers and care teams use the risk-stratification care coordination tool to proactively conduct patient outreach, including telephone calls and telemedicine virtual visits, ensuring patients receive needed social support and medical care during the pandemic.
The Ohio Health Information Partnership (OHIP) was using its technology to share individual COVID-19 test results to providers at the bedside. The State Department of Health approached OHIP with a request to aid the state by providing a more comprehensive data set for COVID-19. Using the Health Catalyst® Data Operating System (DOS™) platform, OHIP has improved the effectiveness of public health reporting and COVID-19 surveillance.
Improving Population Health: Data-Driven Approach to Identifying and Engaging Patients with High Risk of Mortality from COVID-19
Leveraging the Health Catalyst® Data Operating System (DOS™) platform and the ACO Risk Stratification Dashboard, MemorialCare developed and implemented an algorithm to identify and risk-stratify members at the highest risk of mortality from COVID-19. Care managers can visualize at-risk members and the specific factors contributing to the increased risk, allowing them to quickly prioritize member lists for outreach—improving population health and decreasing mortality.
Thibodaux Regional Health System recognized its patients with Type 2 diabetes had hemoglobin A1C (HbA1c) levels that exceeded the evidence-based guidelines for blood glucose control and sought to improve the health of this patient population. Using a data platform and a consistent improvement methodology, Thibodaux Regional learned more about the challenges to diabetes self-management in its population. The organization was then able to improve its outreach and support for patients with diabetes.
Christiana Care Health System (CCHS) had used a machine learning model to inform population segmentation. The initial model used “black box” algorithms to predict risk that care managers didn’t have input on or understand. CCHS leaders and experts wanted an efficient model that they understood and trusted to predict 90-day inpatient admission. CCHS used a feature selection process to build the simplest model possible—and AI insight tools for selecting the best model, setting triggers for action, and explaining how the model worked.
Thibodaux Regional Medical Center conducted a community health needs assessment (CHNA) as part of its vision for a healthier community. The CHNA reinforced the hospital’s identified need for a data-driven, medically integrated wellness program designed to benefit the health of the community by educating, advocating, and supporting healthy lifestyle changes to address modifiable health risks. Using a wellness analytics application, Thibodaux Regional is able to track program outcomes.
To optimize the impact of its diabetes self-management education program, Allina Health enhanced its service model, aligning resources to proactively meet patient demand, while also maintaining high-quality clinical outcomes. Utilizing analytics in the redesign process has allowed Allina Health to understand patient needs better and monitor the impact of planned changes to the program on patient outcomes.
Improving the management of chronic diseases for patients is crucial for reducing expenses and improving health outcomes. Newton-Wellesley Hospital, a member of the Partners HealthCare system, adopted the population health coordinator role and utilized analytics to help identify variations in chronic disease management across practices and develop standardized best practices aimed at reducing costs through better outcomes for patients.
Increased Visibility into Value-Based Performance Results in $2.1M in Additional Pay for Performance
Data-driven decisions and analytics are critical for organizations and physician practices attempting to thrive under value-based care. With the help of data analytics, UTMB Health was able to focus on improvement efforts for specific patient populations and boost reimbursement based on DSRIP performance.
Offering a competitive healthcare plan for employees is a business essential, and a differentiator for organizations to attract top talent, but as healthcare costs continue to rise, employers are increasingly challenged to offer affordable employee healthcare with extensive benefits. Learn how Health Catalyst embraced self-insurance to take the management of its healthcare costs and benefit design into its own hands.
To succeed as a coordinated care organization and better serve its Medicaid population, Health Share of Oregon leveraged analytics to obtain a holistic evaluation of the drivers of per member per month payment performance.
There are more deaths from lung cancer than from any other type of cancer—more than 155,000 deaths annually. Learn how Mission Health utilized its analytics platform to improve the screening and outcomes for patients with lung cancer.
On an annual basis, ACOs are required to accurately report data that is used to assess quality performance which is also linked to eligibility to share in any savings generated. Read how Mission Health implemented a proactive approach to measure and evaluate performance, including widespread adoption of analytics and shared responsibility for ACO measure performance, enabling the organization to sustain and further improve its performance across multiple ACO measures.
Growth in the government payer mix and an increased cost burden to the commercial population, decreases in the private payer population, and programs like the Medicare Shared Services Program, have caused joint ventures, partnerships, and co-branding efforts, better known as at-risk contracts, between payers and providers to increase.
Allina Health has three Integrated Health Partnership (IHP) contracts, an accountable care model that incentivizes healthcare providers to take on more financial accountability for the cost of care for Medicaid patients, which cover approximately 90,000 members. To achieve success in its IHP contracts, and avoid losses, Allina Health needed to reduce healthcare costs while improving patient outcomes and experience.
Allina Health has integrated several data sources, including claims and developed the infrastructure required to perform opportunity analysis. Using data and analytics for opportunity analysis has given Allina Health insight into its IHP patient population, supporting the development of interventions to decrease the total cost of care and improve outcomes.
Health equity means that everyone has an equal opportunity to live the healthiest life possible – this requires removing obstacles to health. The U.S. ranks last on nearly all measures of equity, as indicated by its large, disparities in health outcomes. Illness, disability, and death in the United States are more prevalent and more severe for minority groups. Health inequities persist in Minnesota as well, which motivated Allina Health to take targeted actions to reduce inequities.
Allina Health needed actionable data to identify disparities and to reduce these inequities. This came in the form of REAL (race, ethnicity, and language) data, which Allina Health analysts used to visualize how health outcomes vary by demographic characteristics including race, ethnicity, and language. To understand the root causes of specific disparities as well as to identify solutions within their sphere of influence as a healthcare delivery system, Allina Health consulted the literature and also consulted patients, employees and community members. Then Allina Health created appropriate interventions based on this information.
As a result, Allina Health created an awareness of the health inequities among its patient populations, as well as effective approaches to breach the barriers that were preventing these patients from getting the care they needed. While much work remains in this long journey to achieve health equity, Allina Health has taken some significant steps forward, including:
Three percent relative improvement in colorectal cancer (CRC) screening rates for targeted populations, exceeding national CRC screening rates by more than ten percentage points.
REAL data embedded in dashboards and workflow to easily identify and monitor disparities.
Data-driven decisions and analytics are critical for organizations and physician practices transitioning to value-based care, although many organizations struggle with measuring the effectiveness of these population health initiatives.
To obtain sophisticated, actionable analytics and automate processes, Acuitas Health deployed the Health Catalyst® Patient Intake and Care Coordination applications concurrent with beginning the implementation of the Health Catalyst Data Operating System (DOS™) platform. Acuitas meets the needs of its customers through a sprint to value—going faster than the typical time to value. The concurrent implementation approach used in this roll out set the pace for that sprint to value. In less than 60 days, the organization successfully implemented these tools and began receiving value. Acuitas is now able to:
Collect discrete data, and begin enhancing the work of the integrated care management team in a user-friendly way.
Identify individual caseloads.
Instantly obtain a complete, comparative, real-time picture of caseloads across the team—this reporting took weeks to compile in the past.
Make data-driven decisions on how to improve outcomes.
Influenza, a contagious respiratory illness spread by droplets, can lead to hospitalization and even death. Millions of people get influenza each year, hundreds of thousands are hospitalized, and thousands to tens of thousands die from influenza related causes each year. The key to preventing a devastating outbreak is vaccinating enough people that an outbreak is unlikely.
When Allina Health identified that its own rates for influenza vaccination were lower than desired, the health system studied data gleaned from its EHR and an Analytics Platform from Health Catalyst, which includes a Late-Binding™ Enterprise Data Warehouse and broad suite of analytics applications, to understand its true current vaccination performance. The data revealed that changes were in order, which Allina put in place through clinician feedback, engagement, and education.
4.8 percentage point improvement in influenza vaccination rate, exceeding the Healthy People 2020 goals for vaccination.
Nationally, hospitalization for persons with mental health disorders has increased faster than hospitalization for any other condition. Of concern is the lack of bed space to intake these patients on a timely basis. In Minnesota, for example, more than 50 percent of available state psychiatric beds were closed between 2005 and 2010. Furthermore, readmission rates for patients with mood disorders is higher than any other mental health condition, with 15 percent readmitted within 30 days of hospital discharge and up to 22.4 percent of patients with schizophrenia being readmitted. While the average cost of a readmission in the U.S. is approximately $7,200, of greater concern is hospital readmission represents poor patient outcomes related to lack of adequate access to community mental health resources and challenges with adherence to care plans needed to prevent chronic relapse.
In response to these challenges, Allina Health put a new care transition process in place, redesigned workflow, and added key patient support roles. To measure the effectiveness of new interventions, Allina relied on the Health Catalyst Analytics Platform, which includes the Late-Binding™ Enterprise Data Warehouse and a broad suite of analytics applications.
27 percent relative reduction in potentially preventable readmission rate.
80 percent patient retention rate in established outpatient mental health services.
Patients with diabetes are at a high risk for infections and substantial complications, including the risk of death from infections. Further, social determinants in these patients’ communities have a tremendous influence on their health.
Texas Children’s Hospital, ranked as one of the top four Best Children’s Hospitals by U.S. News & World Report, recognized that there were gaps in diabetes care coordination in the community—where the majority of a child’s diabetes management takes place. The hospital initiated a coordinated community response, aided with an analytics platform, which is setting the standard for community management of pediatric diabetes.
4 percent relative improvement in the percentage of patients with diabetes who received the influenza vaccine.
3 percent relative improvement in pediatric provider diabetes knowledge.
90 percent of patients now have individualized school packets developed and available in the EHR.
Texas Children’s Hospital is improving the care delivery of its patients with diabetes, one of the most common diseases in school-aged children. How? Powered by dedicated improvement teams and analytics, they have focused on order utilization, timeliness of IV and subcutaneous insulin administration, length of stay (LOS), establishing a diabetic care unit (DCU), educating core diabetic nurses (CDNs), frontline staff adoption, and more.
Care delivery improvements include the following:
94 percent of patients with diabetic ketoacidosis (DKA) are assigned to diabetic care unit.
17 percent relative increase in patients with DKA receiving an evidence-based evaluation and order sets.
19 percent relative increase in patients with DKA receiving IV insulin within one hour of order.
50 percentage point improvement in the percentage of patients transitioning to SubQ insulin in less than four hours after medical readiness.
44 percent relative decrease in LOS for patients with DKA.
Each year, more than 12,700 pediatric patients are diagnosed with diabetic ketoacidosis (DKA), a life threatening complication of diabetes. Texas Children’s Hospital sought a way to accurately predict risk of DKA in time for care team members to intervene before these patients suffered a severe episode.
The health system ultimately formed a multidisciplinary high risk diabetes team to devise pre- and post-discharge strategies, and DKA risk prediction tools aided by the Health Catalyst Analytics Platform built using the Late-BindingTM Data Warehouse.
30.9 percent relative reduction in recurrent DKA admissions per fiscal year.
90 percent of all patients with new onset type 1 diabetes at the Medical Center Campus have a documented RIPGC in their medical chart.
100 percent of patients with type 1 diabetes have a risk index for DKA documented every 6 months.
Diabetes is the most common chronic illness for children living in developed countries. Leaders at Texas Children’s Hospital wanted to take a more data-driven approach to population health management for children with diabetes. They created a Care Process Team (CPT) to pursue outcomes improvements related to diabetic ketoacidosis (DKA) since data from the EDW revealed that 64% of diabetes patients discharged had this life-threatening condition.
After the CPT achieved their initial goal of improving care for patients admitted to the hospital with DKA, they set out to implement larger improvements that would benefit the entire population of diabetes patients.
By empowering CPT members, leveraging data to drive decisions, and implementing new interventions effectively, the Diabetes CPT members have improved population health for patients with diabetes across all settings of care. Below are a few of the most significant results.
44 percent relative decrease in LOS for patients with DKA.
30.9 percent relative reduction in recurrent DKA admissions per fiscal year.
34.4 percent relative improvement in the percentage of patients with diabetes who receive the influenza vaccine.
Effectively educating pediatric and adolescent patients and families to self-manage diabetes is a critical part of diabetes care. Leaders at Texas Children’s Hospital, one of the top four children’s hospitals in the country, recognized that diabetes self-management education that incorporates national standards and empowers patients can improve clinical outcomes and quality of life. While diabetes education has always been important to Texas Children’s, the education provided to patients was varied, no organizational standards existed, and tracking the effectiveness was not possible.
To address these challenges, Texas Children’s created an Education Care Process Team (CPT) that focused on: developing a standard education curriculum based on national guidelines, creating consistent education materials, leveraging powerful analytics to identify potential learning gaps and customize patient goals, and investing in the professionals who deliver education.
As a result of these efforts, Texas Children’s achieved the following:
Implementation of a standard diabetes education curriculum.
100 percent of diabetes educators are now CDEs.
70.7 percent of patients with diabetes have had an education visit with a CDE, and the hospital is on track to achieve its goal of 80 percent within the year.