A Roadmap for Optimizing Clinical Decision Support

Compared to industries such as aerospace and automotive, healthcare lags behind in decision support innovation. Following the aerospace and automotive arenas, healthcare can learn critical lessons about improving its clinical decision support capabilities to help clinicians make more efficient, data-informed decisions:

1. Achieve widespread digitization: Healthcare must digitize its assets and operations (patient registration, scheduling, encounters, diagnosis, orders, billings, and claims) for effective CDS similarly to how aerospace digitized the aircraft, air traffic control, baggage handling, ticketing, maintenance, and manufacturing.
2. Build data volume and scope: Healthcare must collect socioeconomic, genomic, patient-reported outcomes, claims data, and more to truly understand the patient at the center of the human health data ecosystem.

Four Keys to Increase Healthcare Market Share

With leadership alignment, easy access to data, and a roadmap to reach their objectives, health systems can drastically increase revenue and grow market share by applying four principles:

• Key 1. Alignment.
• Key 2. Vehicles.
• Key 3: Five tools: access to data, data acumen; finance, vision to execution, and prioritizing outcomes.
• Key 4: Education.

Access to the right data can drive changes that generate $48M in revenue, surpassing the year three market share goals in year two.

Healthcare Data Literacy: A Must-Have for Becoming a Data-Driven Organization

The journey for healthcare organizations to become data driven is complex but absolutely critical for success in today’s increasingly digitized environment. Data literacy is an essential capability because it empowers team members at every level of the organization—from individual learners to executives—to aggregate, analyze, and utilize data to drive decision making. To optimize data usage and reach high levels of data literacy, health systems can create a data literacy program based on four foundational elements:

1. Infrastructure
2. Access
3. Support
4. Privacy and Security

ICD-10 PCS: Harnessing the Power of Procedure Codes

The transition to ICD-10 in 2015 saw the number of available procedure codes increase from roughly 3,000 to more than 70,000. This change gives clinicians the ability to code procedures to a much higher degree of specificity and provides health systems the ability to unlock powerful clinical insights into how inpatient procedural care is delivered. This article covers the benefits and drawback of ICD-10 PCS, as well as concrete ways health systems can use these procedure codes to provide new clinical insights. The article also walks through the anatomy of the seven-digit alphanumeric codes and provides specific clinical examples of how healthcare organizations can slice and dice this data.

When the Promise of Prehabilitation Meets the Power of Healthcare Analytics

Patients who undergo surgery frequently follow a rehabilitation program afterwards to promote recovery. However, starting this program before the procedure may help further accelerate recovery time. Prehabilitation is defined as physical or lifestyle preparation that happens before surgery and is designed to help patients regain function in less time. Prehabilitation includes the following four main components:

1. Medical optimization of pre-existing medical conditions.
2. Physical fitness.
3. Nutritional status.
4. Psychological support.

Providing coordinated care from the pre-surgery period to post-operative recovery helps ensure the best patient outcomes. Additionally, health systems can glean important insights about best practices when they effectively follow the patient journey and capture relevant data throughout.

Health Catalyst^® Introduces Closed-Loop Analytics™ Services

Healthcare organizations face provider dissatisfaction, lack of data integration, and excessive clicks to perform basic functions within the EHR. Closed-Loop Analytics™ aggregates data, circulates that data into new or existing workflows, and then surfaces best practice alerts at the decision point for physicians, clinical providers, and financial and operational teams. With clear calls to action throughout the workflow, organizations improve the utilization and effectiveness of analytics tools, yielding simplified workflows, decreased clicks, and improved outcomes.

Emergency Department Quality Improvement: Transforming the Delivery of Care

Overcrowding in the emergency department has been associated with increased inpatient mortality, increased length of stay, and increased costs for admitted patients. ED wait times and patients who leave without seeing a qualified medical provider are indicators of overcrowding. A data-driven system approach is needed to address these problems and redesign the delivery of emergency care. This article explores common problems in emergency care and insights into embarking on a successful quality improvement journey to transform care delivery in the ED, including an exploration of the following topics:

• A four-step approach to redesigning the delivery of emergency care.
• Understanding ED performance.
• Revising High-Impact Workflows.
• Revising Staffing Patterns.
• Setting Leadership Expectations.
• Improving the Patient Experience.

Effective Healthcare Decision Support for Leaders at Every Level: Three Problems that Leading Wisely Solves

In the high-pressured world of value-based care, healthcare leadership is more important than ever. Leaders need to make data-driven decisions and respond to the increasing demands of patients while cutting costs. The right tools–ones that support today’s outcome-focused healthcare environment–can help leaders break through the noise to make effective decisions. This article shares three common problems faced by healthcare leadership and how Leading Wisely, a decision support tool helps them make informed decisions and guide their organizations forward. Three effective strategies for healthcare leadership include:

1. How to prevent communication breakdown.
2. How to break through measure madness.
3. How to alleviate information overload and siloed reporting.

Healthcare Dashboards vs. Scorecards: Use Both to Improve Outcomes

Healthcare IT leaders tend to debate over which tool is best for measuring and sustaining outcomes improvement goals: healthcare dashboards or scorecards. But using both tools is the most effective approach. “Scoreboards” take advantage of the high-level, strategic capacity of scorecards and the real-time, operational functionality of dashboards. But using both effectively requires a thorough understanding of the who, what, when, and how of each tool.

• Who: Scorecards are for leaders; dashboards are for the frontline.
• What: Scorecards are strategic; dashboards are operational.
• When: Scorecards are daily, weekly, or monthly reports; dashboards are real-time or near real-time.
• How: Scorecards enforce accountability and provide actionable data; dashboards provide drill-down capability and inform root cause.

Despite the different but equally important aspects of each tool, they best support outcomes improvement when used together.

Prioritizing Healthcare Projects to Optimize ROI

Healthcare organizations have long relied on traditional benchmarking to compare their performance to others and determine where they can do better; however, to identify the highest ROI improvement opportunities and understand how to take action, organizations need more comprehensive data. Next-generation opportunity analysis tools, such as Health Catalyst^® Touchstone™, use machine learning to identify projects with the greatest need for improvement and the greatest potential ROI. Because Touchstone determines prioritization with data from across the continuum of care, users can drive improvement decisions with information appropriate to their patient population and the domains they’re addressing.

Care Management Analytics: Six Ways Data Drives Program Success

To succeed in improving outcomes and lowering costs, care management leaders must begin by selecting the patients most likely to benefit from their programs. To identify the right high-risk and rising-risk patients, care managers need data from across the continuum of care and tools to help them access that knowledge when they need it. Analytics-driven technology helps care managers identify patients for their programs and manage their care to improve outcomes and lower costs in six key ways:

1. Identifies rising-risk patients.
2. Uses a specific social determinant assessment to capture factors beyond claims data.
3. Integrates EMR data to achieve quality measures.
4. Identifies patients for palliative or hospice care.
5. Identifies patients with chronic conditions.
6. Increases patient engagement.

Introducing Touchstone: The Next-Generation Healthcare Benchmarking and Opportunity Prioritization Tool

To do healthcare benchmarking effectively and efficiently, healthcare organizations need to know where they’re underperforming, where they’re performing well, and how to focus and prioritize their improvement efforts. They also need a new approach to benchmarking that isn’t limited to the inpatient setting. The Health Catalyst^® Touchstone™ product is the next-generation healthcare benchmarking and prioritization tool that delivers what antiquated benchmarking technologies cannot:

• Risk-adjusted benchmarking across the full continuum of care.
• Artificial intelligence-powered recommendations.
• Ranked lists of improvement opportunities.
• Detailed analytics and an intuitive user interface that enable the easy exploration of factors driving performance issues.
• Democratized benchmarking that’s available to as many people as the organization wants.

Touchstone was designed with many users and use cases in mind, from population health analysts looking to improve ACO performance to C-suite leaders who need a data-driven approach to prioritizing improvement opportunities.

How to Apply Machine Learning in Healthcare to Reduce Heart Failure Readmissions

One large healthcare system in the Pacific Northwest is moving machine learning technology from theory to practice. MultiCare Health System is using machine learning to develop a predictive model for reducing heart failure readmissions. Starting with 88 predictive variables applied to data from 69,000 heart failure patient encounters, the machine learning team has been able to quickly develop and refine a predictive model. The output from the model has guided resource allocation efforts and pre-discharge decision making to significantly improve patient care management activities. And the data has engendered trust among clinicians who rely on it the most for clinical decision making. This inside look at the application of advanced technology offers lessons for any healthcare system planning to ramp up its machine learning and predictive analytics efforts.

Machine Learning in Healthcare: How it Supports Clinician Decisions—and Why Clinicians are Still in Charge

Machine learning in healthcare is transforming healthcare with its ability to tackle data variability and complexity. Everyone in healthcare should embrace this new technology and its ability to deliver more precise, faster, data-driven insight to clinical teams. But just as machine learning has benefits, it also has limitations; for example, it loses its impact when implemented without realistic expectations or without thorough integration with existing clinical processes. As the FDA works to publish guidance on digital health services, including governance regarding the use of algorithms to support clinical decisions, it’s important for everyone in the industry to hold themselves accountable for the quality of the data and the processes that put this data in front of clinicians. Machine learning is transforming the way health systems deliver care to patients by surfacing insights to clinicians at the point of care; but, ultimately, the clinician considers the entire clinical picture to determine the most appropriate plan for patients.

Healthcare Decision Support: A Modern Tool for Today’s Chief Nursing Officer

Chief Nursing Officers (CNOs), essential members of health system C-Suite teams, need healthcare decision support to align nursing resources with systemwide goals. Although nursing’s purpose hasn’t changed, the tools and skills needed to achieve it have. In today’s data-driven, increasingly complex care environment, nursing leaders rely on skills that extend beyond their initial training as nurses; they need expertise in finance, IT, and analytics, among other areas. CNOs, like Faye of Pennington Health, depend on healthcare decision support systems for easy access to data that helps them identify and prioritize the best opportunities, address challenges, and improve outcomes. CNOs who embrace the fact that advanced analytical tools are critical to improving care quality and reducing care costs are poised to effectively lead their systems toward achieving financial, strategic, clinical, and operational objectives.

Why Healthcare Decision Support Is No Longer Optional for Chief Operating Officers

Without daily access to healthcare decision support, health system COOs struggle to make rapid, meaningful decisions. Healthcare decision support systems are no longer optional for these highly visible leaders, who play critical roles in their organizations’ success, for many reasons:

• Aggregates reliable, up-to-date information from all available sources.
• Presents information in user-friendly, user-configurable ways.
• Makes trends and important conclusions more recognizable and understandable.
• Enhances C-Suite’s ability to drill down into data in search of a problem’s root cause.
• Improves C-Suite communication and collaboration.
• Unites C-suites around a common vision and strategy.

Healthcare COOs (and other C-Suites) need healthcare decision support to be data-driven problem solvers and collaborative leaders who achieve clinical, financial, and operational success for their systems. Given the industry’s increasing complexity, healthcare decision support is now an industrywide imperative.