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Integration of Community Health Workers Improves Care Management Effectiveness

“The addition of CHWs to our “triad model” of care management (registered nurses (RNs), social workers (SWs), community health workers (CHWs) as team leads) has allowed iCMP to expand its reach to support high-risk patients who have significant social needs. The CHWs have helped patients address these needs, allowing the patient to focus on their medical issues. They are real asset to our ever-evolving program.”  – Maryann Vienneau Program Director for Care Management Partners Population Health EXECUTIVE SUMMARY While the delivery of health care is …

Boosting Readiness and Change Competencies Key to Successfully Reducing Clinical Variation

Reducing unnecessary clinical variation is necessary in today’s healthcare market for both clinical and financial reasons. Two major drivers for this are the shift from fee-for-service to value-based reimbursement and the need for improving clinical outcomes such as reducing complications and readmissions. Leaders for UnityPoint Health, a healthcare system serving Iowa, western Illinois, and southern Wisconsin, recognized the importance of reducing clinical variation, and the need to have strong physician champions and robust analytics to effectively support improvement efforts. However, it also realized that without understanding organizational strengths and weaknesses related to adopting change and improving outcomes, it would struggle to successfully implement initiatives that delivered the desired benefits and sustained improvements over time. By consistently integrating information from a readiness assessment, an opportunity analysis, and expert resources, UnityPoint Health was able to establish a prioritization and implementation approach to outcomes improvement that has produced the following results:

• Variable costs were reduced by more than $1.75 million based on the deployment of interventions in sepsis alerts, order sets, and other clinical decision support tools.
• Reductions in length of stay have allowed patients to return home earlier and spend more than 1,000 additional nights in their homes.
• Millions of clicks have been reduced for clinicians based on deployment of new sepsis screening tools.
• 36 percent increase in sepsis screenings completed in the emergency department (ED).
• Sepsis order set utilization in the ED has increased by more than 185 percent.

The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution

Public cloud offerings are making homegrown digital platforms look easier and more affordable to health system CTOs and CIOs. Initial architecture and cost, however, may be the only real benefit of a do-it-yourself approach. These homegrown systems can’t scale at the level of commercial vendor systems when it comes to long-term performance and expense, leaving organizations with a potentially costly and undereffective platform for years to come. Over his 25 years as a health system CIO, Dale Sanders, President of Technology for Health Catalyst, has observed both the tremendous value of healthcare-specific vendor platforms, as well as the shortcomings of homegrown solutions. He shares his insights in a question-and-answer session that addresses pressing issues in today’s digital healthcare market.

Three Principles for Making Healthcare Data Analytics Actionable

Data is everywhere. But without a plan to extract meaning from data and turn insights into action, data can’t impact outcomes. Generating value from data takes work, but it can be done. To create compelling data insights that promote action, health systems can follow three guiding principles for actionable healthcare data analytics as well as hire analysts with seven important skills. Three principles form the foundation for actionable healthcare data analytics:

1. Balance investments.
2. Hire generalists over specialists.
3. Develop a team that’s highly aligned and loosely coupled.

New Generation Activity-Based Costing Accelerates Timeliness of Decision Support

In healthcare, the timely delivery of patient-activity level cost metrics to clinical, financial, and operational leaders is critical; it allows the organization to respond to internal and external shifts and challenges to positively impact financial performance without negatively impacting patient care and the patient experience. UPMC determined that the amount of manual effort needed to overcome the deficits of a suboptimal technical infrastructure and database supporting its cost management system drove many of the delays built into its closing process. After exploring the options to enhance and commercialize its cost management intellectual property, UPMC partnered with Health Catalyst to use the Health Catalyst^® Data Operating System (DOS™) to co-develop and commercialize the CORUS™ suite activity-based costing module. The new, analytics-driven cost management system supported a 50 percent reduction in the time needed to complete month-end close:

• Three-day reduction in time to close.
• Monthly preliminary results are typically reviewed within one business day, affording more time for validation and analysis.
• Executives receive financial data up to three days sooner.
• Reduction of 3.5 FTEs needed to complete the monthly close.
• Reduced 60 human touchpoints and opportunity for error.
• Multiple months of data can now be run simultaneously.
• Provided support for new data-driven governance structure.

Reducing Sepsis Mortality and LOS with Improved Recognition and Treatment Protocols

In the U.S., over 1.5 million people are treated for sepsis annually. One in four people with sepsis die, making improving early identification and providing patients with timely treatment a top priority. Hospitals and health systems continue to look to improve outcomes for patients with sepsis. Allina Health, a not-for-profit healthcare system of 12 hospitals and 90 clinics, all serving patients throughout Minnesota and western Wisconsin, previously implemented a rapid process improvement project using a three-part bundle focused on the early identification of sepsis. However, sepsis mortality rates remained higher than desired. After turning to an analytics platform to replace its burdensome manual review process, Allina Health was able to identify opportunities for improvement and develop evidence-based processes for sepsis identification and treatment. Results:

• 18.6 percent relative reduction in mortality rate and 10.9 percent relative reduction in hospital length of stay (LOS) for all patients with sepsis.
• 30.3 percent relative reduction in mortality rate and 18.4 percent relative reduction in hospital LOS for patients with severe sepsis and septic shock.
• $1.1 million in annual cost savings, the result of efficiencies and substantial reductions in hospital LOS for patients with severe sepsis or septic shock.

Healthcare NLP: Four Essentials to Make the Most of Unstructured Data

Many health systems are eager to embrace the capability of natural language processing (NLP) to access the vast patient insights recorded as unstructured text in clinical notes and records. Many healthcare data and analytics teams, however, aren’t experienced in or prepared for the unique challenges of working with text and, specifically, don’t have the knowledge to transform unstructured text into a usable format for NLP. Data engineers can follow four need-to-know principles to meet and overcome the challenges of making unstructured text available for advanced NLP analysis:

1. Text is bigger and more complex.
2. Text comes from different data sources.
3. Text is stored in multiple areas.
4. Text user documentation patterns matter.

Introducing the Next-Gen Patient Safety Organization

Join us for a special webinar announcing the next generation Health Catalyst Patient Safety Organization (HC PSO) and learn why coupling it with the Health Catalyst Patient Safety Monitor™ Suite—built by patient safety experts for patient safety experts—is such an important differentiator. Leading the product announcement are two experts in patient safety, Michael Barton and Elaine St. James. They will discuss the following:

• Importance of active safety surveillance and analysis to discover safety vulnerabilities that are often overlooked.
• Operational efficiency and organizational risk avoidance available by hosting together the safety analytics and HC PSO.
• Effective safety governance and application of safety best practices that will improve outcomes in a measurable, and sustainable way.
• Integration of analytics, and benchmarking from a health care Data Operating System (DOS).

Systematic, Data-Driven Approach Lowers Length of Stay and Improves Care Coordination

Improving and reducing length of stay (LOS) improves financial, operational, and clinical outcomes by decreasing the cost of care for a patient. It can also improve outcomes by minimizing the risk of hospital-acquired conditions. Faced with declining revenue related to changes in Medicare and Medicaid reimbursements, Memorial Hospital at Gulfport knew additional methods of providing more efficient and cost-effective quality care were needed to maintain long-term success. The organization embraced the challenge of reducing LOS to lower costs and lessen risk for its patients. By adopting a systematic, data-driven, and multi-pronged approach, Memorial has achieved significant results in one year including:

• $2 million in cost savings, the result of decreased LOS and decreased utilization of supplies and medications.
• 47-day percentage point reduction in LOS.
  • Improved care coordination and physician engagement have successfully reduced LOS.
  • The 30-day readmission rate has remained stable.
• Three percent increase in the number of discharges occurring on the weekend.

Healthcare Safety Culture: A Seven-Step Success Framework (Executive Report)

Preventable patient harm costs healthcare billions annually, making strategies to improve patient safety an imperative for health systems. To improve patient safety, organizations must establish a safety culture that prioritizes safety throughout the system, supports blame-free reporting of safety events, and ensures that healthcare IT solutions functions and accessibility align with safety goals. A sociotechnical framework gives health systems a seven-part roadmap to improving patient safety culture:

1. Leverages qualitative and quantitative data.
2. Doesn’t rely on HIMSS stage levels to tell the complete safety picture.
3. Gives frontline clinicians a voice in decision making.
4. Makes IT solutions accessible to non-technical users.
5. Encourages frontline clinicians to report safety and quality issues.
6. Treats a safety issue in one area as a potential systemwide risk.
7. Performs thorough due diligence before taking safety IT solutions live.

Four Critical Phases for Effective Healthcare Data Governance

Based on a 2018 Healthcare Analytics Summit, this report details the four phases necessary for successful healthcare data governance:

1. Elevate a vision and agenda that align with organizational priorities.
2. Establish an organizational structure to fulfill the data governance mandate.
3. Execute with prioritized data governance projects, people and resource assignment, and disciplined focus on the work.
4. Extend data governance investments and efforts through established practices.

Readiness Assessment Key to Sustainable Outcomes Improvement

Experiencing pockets of success is not enough to prosper during the transformation to value-based care. Leaders at UnityPoint Health, a healthcare system serving Iowa, western Illinois, and southern Wisconsin, determined that outcomes improvements needed to be sustained and spread easily across the organization to best utilize resources and serve its patients. UnityPoint Health required an objective way to understand the strengths and weaknesses of the organization relative to outcomes improvement and its readiness for change. To this end, it chose the Health Catalyst^® Outcomes Improvement Readiness Assessment (OIRA) Tool and professional services to administer it and identify the competency levels in the organization in the five areas known to influence an organization’s readiness for change. This resulted in:

• Competency for improving outcomes measured at the organization, department and role level.
• Recommendations made for increasing competency levels across the organization.
• Clear direction and focus obtained from opportunity analysis.

The Top Seven Healthcare Outcome Measures and Three Measurement Essentials

Healthcare outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this article adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples. The top seven categories of outcome measures are:

1. Mortality
2. Readmissions
3. Safety of care
4. Effectiveness of care
5. Patient experience
6. Timeliness of care
7. Efficient use of medical imaging

How to Turn Data Analysts into Data Scientists

Healthcare data scientists are in high demand. This shortage limits the ability of healthcare organizations to leverage the power of artificial intelligence (AI). Health systems must better utilize their data analysts, and, where possible, turn some data analysts into data scientists. This report covers the following:

• Healthcare use cases and which ones data analysts can take the lead on.
• Specific steps for turning data analysts into data scientists.
• How to identify the best candidates among your data analysts.
• Recommended resources to get started on an AI journey.

Machine Learning Improves Accuracy of Risk Predictions and Improves Operational Effectiveness

The Centers for Medicare and Medicaid Services (CMS) readmission penalties are a significant concern for healthcare organizations, with over 2,500 hospitals being penalized each year, resulting in CMS withholding more than $500 million in payments. For Westchester Medical Center Health Network (WMCHealth), hospital readmissions carried more than financial consequences. Care managers had to use multiple systems and time-consuming, manual processes to identify recently discharged patients at risk for readmission. These processes limited the effectiveness of the care management team, as care managers lost valuable time searching patient records for data needed to prioritize their workload and choose the right interventions. To address this problem, the data analytics teams at WMCHealth and network member Bon Secours Charity Health System leveraged artificial intelligence and machine learning to develop a more accurate readmission risk prediction model that would enable care managers to use their time coordinating and engaging with patients more effectively. Results include:

• A risk prediction model that is 17 percent more accurate than widely used readmission risk models in identifying patients at high-risk and low-risk for readmission within 30-days.
• Care managers obtain follow-up appointments faster, usually within seven days, and connect patients with the services needed to prevent unnecessary visits to the emergency department and readmissions to the hospital.
• 1,327 hours per year saved, freeing up care managers to spend more time with patients.

A Systematic Framework for the Delivery of Safe, Highly Reliable Care and Habitual Operational Excellence

Drs. Frankel and Leonard will provide a practical, sociotechnical framework that has evolved from their experience in training over 2,500 patient safety officers and working with numerous, world-class health systems over the last two decades. The framework focuses on effective leadership, a culture of safety and continuous learning. Join this webinar to hear practical methods of assessment, engagement and implementation that can be readily applied in virtually any care setting. Webinar attendees will learn:

• Specific components necessary for the delivery of high value care.
• The critical importance of psychological safety and how to achieve it.
• How to assess any care environment for inherent strengths that can be utilized to enhance learning and mitigate risk.
• Practical tools and behaviors that can be immediately applied in pursuit of habitual, operational excellence.

DSRIP in 2018: Continuing Efforts for Medicaid Reform

As a performance-based incentive program, DSRIP (the Delivery System Reform Incentive Payment) is designed to help participating states reform Medicaid. To date, 13 states have implemented DSRIP and received a Section 1115 waiver from CMS to transform their Medicaid programs and align them with value-based reimbursement. These states have agreed to budget neutrality, transparency, statewide quality metrics, and frequent reporting of outcomes. While each state’s program structure and objectives are unique, under DSRIP, participating states share three key goals:

1. Reducing the total medical spend.
2. Improving patient outcomes.
3. Establishing a direct link between provider performance and payment.

The Secret to Patient Compliance: An Application of The Four Tendencies Framework

Every day, healthcare professionals face the challenge of determining how to get patients to make good healthcare decisions and follow recommendations. The Four Tendencies framework, developed by The New York Times bestselling author Gretchen Rubin, can make this task easier and improve patient compliance by revealing how each person responds to expectations. By asking this question, healthcare practitioners can gain exciting insights into how patients respond to expectations to in order to help them achieve their goals. This report covers the following:

1. An overview of each of the Four Tendencies.
2. An understanding of how these tendencies can affect behavior in a healthcare setting.
3. Practical tips for working with patients and colleagues that fall into different tendencies.

Meaningful Machine Learning Visualizations for Clinical Users: A Framework

Health systems can leverage the predictive potential of machine learning to improve outcomes, lower costs, and save lives. Machine learning, however, doesn’t inherently produce insights that are actionable in the clinical setting, and frontline clinicians need information that’s accessible and meaningful at the point of care. Thoughtfully designed visualizations of machine learning insights are a powerful way to give clinical users the information they need, when and how they need it, to support informed decision making. A design framework for machine learning visualizations addresses three key questions about who will use the decision-support insights and how:

1. People: who are the targeted users?
2. Context: in what context or environment do they work?
3. Activities: what activities do they perform?

ACOs and CINs — Where Did They Start, How Have They Evolved, and Where Are They Going Next?