Renown Health’s data were contained in disparate sources, delaying access to data, and resulting in different data definitions and interpretations of data. Renown Health integrated its data, creating one source of truth for the organization with the Health Catalyst® Data Operating System (DOS™), enabling it to deliver timely, quality care, which is especially needful to address the COVID-19 pandemic.
Multiple Data Source Integration
Community Health Network (CHNw) desired to understand the financial, provider, and overall impact of COVID-19 related declines in elective surgeries. The data needed to understand the impact and prioritize the organization’s elective surgery restart plan resided in disparate systems, requiring hundreds of hours of manual data review. Using data and analytics, CHNw is able to plan how to optimally meet its patients’ needs and effectively recover from COVID-19 revenue loss.
Banner Health, regarded as a top health system in the country for clinical quality care, was well prepared to respond to the COVID-19 pandemic and had suspended elective surgeries to help keep resources available for patients with COVID-19. Leveraging Health Catalyst’s data platform and analytics, Banner Health has the integrated clinical, financial, and operational data required to develop the organization’s elective surgery financial plan.
MultiCare Health System activated its incident command structure and set out to use the EMR to support its critical data and analytics needs to manage a systemwide organizational response to COVID-19. The organization quickly identified that the EMR could not integrate data from disparate sources or provide a systemwide dashboard. It leveraged data and analytics to create a COVID-19 dashboard, allowing the organization to quickly visualize the data required to effectively plan for, and manage, the health system’s response to COVID-19.
Acuitas Health improved access to data for its partner clinicians by using its data platform and closed-loop analytics to integrate data from more than ten disparate systems. Clinicians receive patient-specific details before the patient visit, allowing them to identify opportunities for health maintenance, improve quality, support data-driven medical decision making, increase adoption of best practices, and improve hierarchical condition category (HCC) coding.
Billings Clinic had its data located within multiple different source systems, which limited access to the data and decreased trust in the data. The available tools were difficult for non-analysts to use and understand, creating resistance to self-service analytics. To breakdown data silos, ensure a gold standard for metrics, and optimize its analytics use, Billings Clinic deployed a data platform and analytics application across its organization.
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.
Albany Med’s clinical documentation improvement specialists provide high-quality care to complex, acute-care patients; however, Albany Med was experiencing lower reimbursement rates due to gaps in clinical documentation. The organization created a seamless process for clinical documentation with the use of an analytics application as driven by clinical leadership.
To ensure it continues the widespread use of data and analytics, Allina Health needed a plan to ensure ongoing data utilization and continuous, data-driven improvement, increasing the number of people learning from the valuable data in its data platform. By leveraging an advanced data platform and a robust suite of analytics accelerators, the health system observed significant improvements.
Texas Children’s Hospital knew that improving data access was key to driving improvements and sought to improve analytics adoption and democratize its data. By focusing on developing a culture of data access and sharing, Texas Children’s has shifted its data and analytics culture, establishing the foundation required for it to continue to advance its analytics adoption, including engaging in predictive analytics. Leaders and employees are actively investigating and sharing data, and operations are more data-driven than ever before.
For every hour of direct patient care they provide, primary care physicians spend nearly two hours on EMR tasks. Registered nurses also spend a substantial amount of their time, up to 45 percent, in the EMR as part of their regular workflow. Using closed-loop analytics integrated into its EMR and COPD application, UnityPoint Health has automated and improved workflow, gained operational efficiency, and improved staff satisfaction.
Community Health Network (CHNw) was keenly aware of the needs of the elderly population in its communities of impact. However, despite the development and implementation of a successful geriatric program, the organization lacked access to, and visibility of, meaningful data to quantify program outcomes. The CHNw Geriatric Evaluation and Management (GEM) team used an analytics application to demonstrate the sizeable, positive impact of the GEM team care and interventions on both patient and financial outcomes:
The need for seamless reporting, advanced visualizations, and end-user self-service models is critical to inform decision making. Learn how Orlando Health leveraged its analytics platform and applications to provide users with a data model that could enable a single source of truth for data-driven decision making.
Changing payment models are putting pressure on clinicians to have instant access to actionable information about their patients, their performance, and their potential to close gaps in care. Read how Orlando Health recognized the value of immediate access to adaptive, integrated data, giving users access to rapidly deployed data in consumable, actionable visualizations.
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.
Research shows that despite an increase in the number of improvements in clinical, cost, and operational outcomes, there is a lack of sustained improvements. Some of the key challenges can be access to the data and analytics, and adherence to data-driven clinical standards, things the Allina Health Spine Clinical Service Line (CSL) clinical leadership team experienced.
By providing widespread access to the data and analytics, the Spine CSL at Allina Health has been able to continue its reduction in LOS and further improve its reduction in complications, all while increasing cost savings and achieving pay-for-performance incentives.
$1 million in pay-for-performance incentives received.
More than $2 million in supply chain savings, a result of data-driven clinical standardization.
31 percent of expected complications avoided.
22 percent relative reduction in surgical site infections.
Healthcare reimbursement continues to shift away from fee-for-service reimbursement models to value-based, risk-sharing agreements. This shift has resulted in organizations revising compensation strategies to engage physicians in value-based compensation arrangements. An effective value-based physician compensation plan is critically important, particularly in competitive environments where organizations must optimize the ability to recruit and retain highly skilled providers. One commonly used physician compensation approach includes a base salary and productivity incentives, coupled with additional compensation opportunities for achieving quality and service goals. The physician compensation package at John Muir Health is not only competitive, it is also complex, but the support process was burdensome, inefficient, and lacked transparency.
John Muir Health developed a plan to leverage the Health Catalyst® Analytics Platform, including the Late-Binding™ Data Warehouse and broad suite of analytics applications, to develop an automated process for physician compensation. The plan created efficiencies in time and effort across multiple domains and produced software to automate future work. The benefits included:
Saving 1,560 hours of time required to produce the data necessary to calculate physician compensation.
Successfully integrating more than ten different compensation models and 20 different data elements for more than 300 different providers into the physician compensation analytic application, automating the process.
Mixed reviews of the effectiveness of pay-for-performance programs leave hospitals wondering how to affect meaningful change in patient care and outcomes. However, MultiCare’s experience with focused improvement efforts supported by analytics for pneumonia, sepsis, and women’s care showed that better data consistently leads to better patient outcomes.
Committed to improving population health, and informed by their experience as well as national trends and outcomes, MultiCare formed a new partnership with Health Catalyst, a next-generation data, analytics, and decision support company. The shared risk partnership generated an improvement framework and governance structure formed around a Shared Governance Committee which is responsible for prioritizing, resourcing, and aligning improvement initiatives across MultiCare. The committee and the projects it ultimately approves are informed by data-driven opportunity analysis and ongoing analytics support. This partnership and structure have achieved the following:
Strategic alignment of outcomes goals across the organization.
Established an Analytics Center of Excellence.
Integrated financial data into outcomes improvement initiatives.
Effective data integration enables high value through more strategic, data-driven decision-making, while faster data acquisition feeds and speeds up the process. Orlando Health, one of Florida’s most comprehensive private, not-for-profit healthcare networks, recognized the need for effective data integration to successfully manage to the organization’s changing business needs. The health system needed the ability to rapidly acquire and link disparate healthcare data sources in various ways in order to answer clinical and business questions.
Leaders at Orlando Health needed a data warehouse that better met their needs. They determined that switching from an early binding data process to a late-binding process would provide greater flexibility and expand their access to critical data, with shorter data acquisition times.
With the new EDW, Orlando Health achieved the following efficiencies:
245 fewer days and 1.0 less full time employee (FTE) needed to integrate encounter billing summary system data.
56 fewer days and 0.4 less FTE needed to integrate Infection control system data.
99 percent reduction (90 days saved) in the amount of time needed to implement system enhancements.
98 percent reduction in the work hours needed to incorporate system enhancements.
Clinical variation can be frustrating for patients and their families, often leaving the impression that healthcare team members are not on the same page and don’t agree on the plan for the patient’s diagnosis or treatment. It is also costly—the Institute of Medicine estimates that $265 billion (30 percent) of healthcare spending is waste that directly results from clinical variation.
To reduce unwanted variation, Texas Children’s Hospital invested considerable resources to develop clinical standards tools, including evidence-based order sets; however, demonstrating the effectiveness and utilization of those guidelines, pathways, and order sets had been daunting. To that end, Texas Children’s deployed an analytics platform from Health Catalyst to aggregate and analyze the data needed to perform both of these critical functions.
$2,401 reduction in cost per patient with order set utilization, and an 8.4-day difference in average length of stay (LOS).
$15 million reduction in total direct variable costs in Fiscal Year 2015, $32 million anticipated reduction in Fiscal Year 2016 at the current order set usage rate, and a potential $64 million annual reduction with a hypothetical 80 percent order set usage rate.
1,629 percent return on investment (ROI).
A hospital’s core mission is to provide the best care possible. To continue to do so, however, hospitals must be paid promptly. Discharged not final billed (DNFB) cases—where bills remain incomplete due to coding or documentation gaps—represent an ongoing challenge for hospitals around the country.
Thibodaux Regional Medical Center, like other hospitals, faces a myriad of new government regulations that have made hospital bill collection efforts more onerous. Its leaders recognized their inadequate manual DNFB process left hospital staff overburdened and put at risk the necessary cash flow to best serve patients.
The hospital automated and streamlined this process to relieve the burden on physicians, provide an integrated view of data, optimize visibility and workflow, and reduce the need to “downcode” reimbursements due to missing documentation. The hospital leveraged analytics to provide actionable feedback to continuously improve the process.
Thibodaux has already achieved significant improvements to cash flow and operational efficiency:
44.4 percent improvement in delinquency rate
8.2 days reduction in A/R days
70.5 percent decrease in the number of billhold accounts outstanding
50 percent decrease in physician portion of DNFB dollars
97 percent improvement in operational efficiency
When healthcare information systems don’t talk to each other, countless inefficiencies and patient safety issues may arise.
Community Health Network (CHNw) believes in delivering outstanding care to every patient. In order to minimize patient safety risks and inefficiencies resulting from using different EHRs, CHNw embarked on a journey to integrate its healthcare information technologies. After implementing a Late-Binding™ Data Warehouse from Health Catalyst that integrates all key data sources, CHNw now has a consistent and comprehensive perspective for multiple patient encounters across the enterprise. It has achieved the following results:
Data from multiple EHR vendors, including four inpatient EHRs and two ambulatory EHRs, plus five transactional systems—HR, patient experience, patient safety, finance, and supply chain— were integrated within 12 months.
More than 55,000 data elements and over 18 billion rows of data were incorporated.
Patient-to-patient matching was implemented for over one million patients across the four inpatient EHRs. This is vital for managing patient populations.
Operational efficiency was improved by 70 percent, with data architects spending an estimated 15 percent of time supporting interfaces compared to an estimated 40-50 percent before the integration. In one example, CHNw linked its ERP/costing system to the EDW’s EHR source marts with just a single interface; previously, this would have required building separate interfaces for all six EHRs.
For patients with the severest form of sepsis, the chance of survival decreases by 7.6 percent for every hour that antimicrobial treatment is delayed. Coordinated team work and the speed with which recognition, diagnosis, and treatment of sepsis occur are critical. Health systems across the country have discovered that by successfully engaging clinicians in driving and maintaining best practice interventions they are able to save lives and improve patient outcomes. At Piedmont Healthcare, the work of educating clinicians on the importance of following sepsis care best practices had been done. The missing pieces were a well-resourced, systemwide improvement team to improve sepsis care, and a concise way to view and give timely feedback on performance based on accurate, trusted data. To fill in these missing pieces, Piedmont created a cross-representative sepsis improvement team and enabled tracking for compliance to best practices with an analytics application from Health Catalyst. Within just three months of deploying the Sepsis Improvement Application, Piedmont has accomplished significant improvements in efficiency—and completely won trust in the data. Piedmont has already identified early indications of patient outcome improvements. Initial achievements of its sepsis improvement team include deploying systemwide visibility into sepsis care performance and best practices compliance, improved acknowledgement of first alert by 19 percent across the system, and a reduction in manual data collection by 97 percent.
U.S. healthcare is shifting from procedure and visit approaches to a longitudinal view of patient care. The Centers for Medicare & Medicaid Services (CMS) is supporting this change with their “Bundled Payments for Care Improvement Initiative.” Under the initiative, healthcare organizations enter into payments arrangements with financial and performance accountability for 48 episodes of care. This requires health organizations to integrate data from a combination of sources in order to identify the bundles with the highest costs and the sources of variation. Learn how Partners HealthCare, an Integrated Healthcare Delivery System and ACO, successfully integrated hospital, provider, and claims information for the first time—and how they can now easily evaluate and compare clinical and financial performance for the 48 CMS episodes of care.
As the healthcare industry rapidly evolves, implementing an enterprise data warehouse has become essential both for population health management and economic survival. While this requires building analytics competency across the enterprise, once adopted, the benefits are abundant—from improved patient outcomes to reduced waste and costs. To rapidly gain value from this platform, healthcare organizations should follow an implementation strategy that, before anything else, identifies the problems analytics is intended to solve. It should also place as much emphasis on people and processes as it does technology. Partners HealthCare is an example of how implementing a data warehouse can quickly leverage analytics across the enterprise to achieve value with high end-user engagement and satisfaction.