Predictive Analytics and Care Management Reduces COVID-19 Hospitalization Rates Avoiding Nearly $2M in Costs

For people 65 years of age and older, COVID-19 hospitalization rates in the U.S. have been as high as 1,245.7 per 100,000 population, straining the resources and capacity of health systems. The limited availability of resources at ChristianaCare impacted its ability to effectively identify patients with COVID-19 who were at risk of severe illness and hospitalization. By leveraging its analytics platform, including data science tools and predictive analytics to provide COVID-19 risk prediction, the organization was able to provide targeted interventions and help patients avoid unnecessary hospitalization.

Five Ways Activity-Based Costing Can Maximize Earnings

Surviving on thin operating margins means health systems must maximize every financial earning opportunity. To identify threats to the revenue stream, organizations need access to precise, accurate costing information. An activity-based costing (ABC) system leverages patient resource utilization data to reveal exactly how much it costs to deliver care. Unlike traditional costing systems that provide average cost estimates for services rendered, ABC includes five benefits that help systems understand the cost for every aspect of the care delivery process:

1. Comprehensive costing data.
2. Ease of use.
3. Precision and accuracy.
4. Near real-time analytics.
5. A proactive cost strategy.

How to Digitize and Democratize Healthcare Data on a Global Scale

Part 2: How AI and Data Can Streamline Healthcare

Part 1: How AI and Data Can Streamline Healthcare

How Data Digitization Can Advance Life Sciences

Your AI Journey Starts Here: A Four-Step Framework for Predictive Analytic Success

COVID-19 has highlighted the imperative for health systems to proactively prepare for future scenarios. One way organizations can ready themselves is by using artificial intelligence (AI), such as predictive analytics, to forecast clinical, operational, and financial needs. While many health systems have the historical and current data they need for predictive modeling, they often lack the requisite analytics foundation and knowledge to begin any AI project, let alone predictive analytics journey. Data and analytics technology lay the foundation to support a health system for a successful AI pursuit, including predictive analytics. With the right tools in place, health systems are ready to follow the four-step framework:

1. Project intake and prioritization.
2. Project kickoff.
3. Model development.
4. Operationalizing the predictive model.

How Regulatory Compliance Supports Optimal Patient Care and Higher Earnings

Hospitals spend over $7.5 million every year on regulatory compliance. Payers, such as CMS, rely on these quality measures to evaluate health system and provider performance and determine reimbursement rates for services rendered. As a result, regulatory performance is critical to the care process and revenue stream. However, many health systems fail to meet these care standards and maximize reimbursement rates because they lack analytic insight into regulatory performance. With a data engine that tracks and submits quality measures data, leaders understand their compliance performance, gaining insight into opportunities to improve patient-centric care and value-based performance. This data-informed approach allows organizations to increase profits through peak regulatory performance and avoid financial penalties associated with underperformance.

AI Can Advance Health Equity

Health technology and augmented intelligence (AI) can significantly improve or worsen health equity. Recently, there has been a growing concern that AI is increasing disparity. ChristianaCare set a goal to reduce avoidable health disparities. The organization faced many challenges, including inconsistent collection, storage, and use of personal characteristics such as race, ethnicity, and language. Using its data platform and Healthcare.AI™, ChristianaCare now has a single “source of truth” for personal characteristics data. By treating health equity as a goal with the same commitment and focus as it would for other clinical, operational, or financial improvement efforts, the organization is purposefully using AI to achieve health equity.

Three Keys to a Successful Data Governance Strategy

With data and data sources on the rise in healthcare, organizations need to more effectively organize, track, and distribute data to team members. A data governance strategy gives health systems a standardized approach to manage data, their most precious asset. Effective data governance helps leaders maximize their data, promote systemwide data-informed decision making, and drive sustainable improvement. Healthcare leaders can operationalize data governance in their organizations by considering three key elements of an effective strategy:

1. Start with the data governance basics.
2. Ensure the data governance strategy supports sustainable improvement.
3. Align the data governance strategy with organizational priorities.

Understanding Population Health Management: A Diabetes Example

Diabetes is one of several chronic health conditions at the root of U.S. healthcare challenges. To improve the quality of care and costs associated with diabetes, health systems, clinicians, and patients can benefit from taking a data-centric approach to diabetes management and leveraging population health tools. Managing individual cases of diabetes require actively involving patients in their care plan, enabling each patient to monitor and understand key data, such as A1c readings, and adjust lifestyle or other factors affecting overall health. Managing diabetes across larger populations, however, is best done through the use of a data and analytics platform that can aggregate data from multiple sources and provide actionable insights. Specifically, a data platform can identify patients who aren’t up to date on tests and those at high risk for other complications, uncover variations in diabetes care across an organization, and more.

Analytics Enables Robust COVID-19 Patient and Staff Contact Tracing

Carle Health was using all available business intelligence resources to meet COVID-19 challenges. The organization quickly realized that the functionality provided by its EMR was insufficient for COVID-19 contact tracing efforts. By utilizing its data platform and the COVID-19 Patient & Staff Tracker, Carle Health is improving the efficiency, effectiveness, and accuracy of its COVID-19 contact tracing efforts.

Four Elements that Bridge the Gap Between Using Data and Becoming Data-Driven

With mounting pressures to deliver quality care with fixed resources, data-driven healthcare is pivotal to organizations’ well-being. From operations to the front lines of clinical care, data can drive the best outcome if decision makers have relevant information when they need it. However, many organizations simply use data in one-off situations rather than integrating it into systemwide processes and workflows. To understand what it means to become data driven and take the right steps forward, organizations can apply four key elements:

1. Invest in one source of data truth.
2. Apply a data governance strategy.
3. Promote systemwide data literacy.
4. Implement a cybersecurity framework.

Five Ways Healthcare AI Gives You Superpowers

As healthcare decisions, data points, and options increase, time, resources, and margin of error decrease. To succeed in this environment, leaders and analysts must know where to focus and how to allocate resources and set accountability targets. With Healthcare.AI™, five super-powered assistive augmented intelligence capabilities help healthcare leaders and analysts determine values, understand context, and provide data-driven motivation to transform healthcare:

1. Enhancing humans’ natural visual pattern recognition.
2. Calculating trajectories.
3. Accelerating the pace at which analysts produce and experiment with how to present the insights.
4. Producing high-caliber, high-quality analytic results.
5. Building trust by enabling immediate, visual, and transparent results.

Drive Better Outcomes with Four Data-Informed Patient Engagement Tactics

Increased patient engagement leads to better clinical outcomes, but organizations still struggle to engage patients and their families in their care. To start, patients have different levels of interest in their care and competency regarding healthcare, which adds to the challenge of treating each patient like a member of the care team. However difficult these patient engagement roadblocks are, organizations can use data to overcome them. Access to data allows healthcare leaders and providers to identify opportunities to optimize patient engagement. By implementing four data-informed tactics, systems can increase patient engagement and improve health outcomes:

1. Implement shared decision-making interventions.
2. Advance health equity.
3. Prioritize patient feedback.
4. Provide patient-centered education.

Six Steps Towards Meaningful, Ongoing Healthcare Performance Improvement

The long-term success of healthcare performance improvement relies on a sustainable infrastructure and strategic execution. Otherwise, improvement initiatives risk becoming one-off projects that don’t support ongoing advances in critical areas, such as critical areas, clinical outcomes, patient experience, and organizational cost. Healthcare organizations can follow six steps for a sustainable, impactful performance improvement program: 1. Integrate performance improvement into strategic objectives. 2. Use analytics to unlock data and identify areas of opportunity. 3. Prioritize programs using a combination of analytics and an adoption system. 4. Define the performance improvement program’s permanent teams. 5. Use a best-practice system to define program outcomes and interventions. 6. Estimate the ROI.

Six Steps Towards Meaningful, Ongoing Healthcare Performance Improvement

The long-term success of healthcare performance improvement relies on a sustainable infrastructure and strategic execution. Otherwise, improvement initiatives risk becoming one-off projects that don’t support ongoing advances in critical areas, such as critical areas, clinical outcomes, patient experience, and organizational cost. Healthcare organizations can follow six steps for a sustainable, impactful performance improvement program: 1. Integrate performance improvement into strategic objectives. 2. Use analytics to unlock data and identify areas of opportunity. 3. Prioritize programs using a combination of analytics and an adoption system. 4. Define the performance improvement program’s permanent teams. 5. Use a best-practice system to define program outcomes and interventions. 6. Estimate the ROI.

Building Analytic Acumen with Less Classroom “Training” and More Learning

Join Sheila Luster-Avant, interim chief data and analytics officer, Froedtert and the Medical College of Wisconsin and Health Catalyst team members Tom Burton, co-founder, Jill Terry, chief learning officer and Eric Denna, senior vice president of professional services, to learn how health systems such as Froedtert and the Medical College of Wisconsin are leveraging the latest learning science to significantly improve the analytics and improvement literacy of leaders, analysts, and improvement teams for less time and money.

What You’ll Learn

• Why Froedtert and the Medical College of Wisconsin needed a new approach to improve their analytic acumen.
• How advances in neuroscience make learning more scalable in healthcare organizations.
• How providing direction and autonomy helps individuals succeed in learning and their roles.
• Best practices from Froedtert and the Medical College of Wisconsin’s experience that you can apply at your organization.

Delivering Precision Medicine: How Data Drives Individualized Healthcare

Delivering precision medicine requires healthcare to transition from a one-size-fits-all methodology to an individualized approach. This means healthcare professionals tailor treatment and prevention strategies according to each patient’s personal characteristics—their genomic makeup, environment, and lifestyle. To realize these precision care goals, researchers and clinicians must leverage vast and varied amounts of real-world data. Data access and interoperability barriers have often impeded the precision medicine transformation. However, current healthcare industry trends increase opportunities for researchers and clinicians to more comprehensively understand medical conditions and the patients in their care. These insights establish the foundation for precision medicine and support actionable pathways towards more efficient development of targeted treatments.

How Data Can Reduce Length of Stay and Keep the Revenue Stream Flowing

Many organizations face high costs and diminishing returns due to unnecessarily high length of stay (LOS) and readmission rates. Elevated LOS and readmission rates can indicate low quality care and also result in costly financial penalties. Therefore, addressing LOS and readmission rates can eliminate avoidable financial consequences, while keeping patients out of the hospital and less likely to develop hospital-acquired infections. Health systems can leverage analytic insight to reduce unnecessary patient LOS and readmission rates, resulting in lower costs for health systems and better health for patients, by applying three data-driven strategies:

1. Implement process changes.
2. Remove discharge barriers.
3. Improve care transitions.