Showing contents for:

Knowledge Center

The Healthcare Analytics Summit™ 2021 Virtual

The Healthcare Analytics Summit™ (HAS) 21 Virtual features internationally recognized speakers, national and global networking opportunities, and traditional HAS fun—including #Socks of HAS, quiz questions, daily scavenger hunts, prizes, and more. In addition, the 2021 global theme will take attendees on a virtual journey to three international destinations—Singapore, Dubai, and London—while exploring the digital trends and best practice experiences driving healthcare success in the new digital era. The HAS 21 Virtual world-class speaker line-up includes the following:

  • Head Coach of the Golden State Warriors and two-time NBA Coach of the Year, Steve Kerr.
  • AI Rana el Kaliouby, PhD, co-founder and CEO of Affectiva and pioneer and inventor of Emotion and Human Perception.
  • Chris Chen, MD, CEO of ChenMed, and Brent James, MD, MStat, Clinical Professor, Clinical Excellence Research Center (CERC), Department of Medicine at Stanford University School of Medicine.

Optimize Your Labor Management with Health Catalyst PowerLabor™

To cut costs, healthcare leaders are looking at their greatest operating expense—labor management. However, with outdated labor management systems, decision makers rely on retrospective, incomplete data to forecast staffing volumes and patient support needs. Limited workforce insight can result in misaligned staffing or worse, jeopardizing patient care due to lack of labor support. With the Health Catalyst PowerLabor™ application, part of the Financial Empowerment Suite™, decision makers have access to a comprehensive view of labor data by organization, department, team, and job role. Timely insight into current and future hospital needs allows leaders to staff to patient volume, control escalating labor expenses, and ensure optimal resources for excellent patient care.

Own Your Value-Based Care Future with the Health Catalyst Value Optimizer™ Solution

As value-based care (VBC) becomes more common in healthcare, population health leaders need a better approach to managing risk-based contracts and optimizing VBC strategies. Unfortunately, many health systems rely on outdated population health offerings that lack data integration capabilities and only provide a fragmented view of their populations. To pinpoint high-priority groups and succeed in VBC, organizations can rely on the Health Catalyst Value Optimizer™ solution. Value Optimizer aggregates and analyzes comprehensive patient data, then instantly identifies the most valuable opportunities for improvement throughout the care continuum. With a full-service solution and increased visibility into performance, leaders can master their VBC and ensure patients receive the best care at the lowest cost.

Delivering the Right Insight to the Right Person: How Workflow Automation Optimizes EHR Decision Support

While the EHR increases the legibility and comprehensiveness of patient health data and makes vital insights more accessible, digitized records also drive longer workflows and hard-to-manage data volumes. Fortunately, the healthcare digital environment today also makes effective data curation achievable. With an automated EHR workflow, healthcare data and analytics technology mines the data platform, bringing the value of digital documentation directly to team members. Automation of routine, repeatable tasks, paired with curation of the most important information in the chart, allows providers and patients to benefit from the wealth of digitized documentation, as workflows ensure the right person accesses the right insight at the right place and time.

The Secret Behind Resilient Healthcare Organizations: High Reliability

Resilience in healthcare means that organizations are continually ready to navigate disruptions of any size without sacrificing quality of care or patient and staff safety. Health systems maintain resilience by embedding the principles of high reliability into their culture, workflows, and processes. These high-reliability organizations (HROs) don’t approach reliability as a short-term project or checklist; rather, they embed the principles into every interaction and action beginning with senior leadership. As a result of a practice, not project, approach to reliability, HROs “rarely fail even though they encounter numerous unexpected events,” as authors Karl Weick and Kathleen Sutcliffe explain in their book series, “Managing the Unexpected.”

A Five-Step Audit for Peak Charge Capture Performance

As health systems strive for financial growth and stability in a pandemic and shifting healthcare market, leaders often overlook a key opportunity to maximize profit margins for services rendered—a charge capture audit. A charge capture audit takes a deep dive into the charge capture process, exposing root causes of costly errors and suboptimal processes. These five steps derive critical insight to help health systems apply interventions and restore revenue integrity:

  1. Set the standard.
  2. Measure current practice against the standard.
  3. Compare the results at the standard to practice.
  4. Change the practice to best practice.
  5. Re-audit.

Five Practical Steps Towards Healthcare Data Governance

Health systems increasingly recognize data as one of their top strategic assets, but how many organizations have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data. By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset:

  1. Identify the organizational priorities.
  2. Identify the data governance priorities.
  3. Identify and recruit the early adopters.
  4. Identify the scope of the opportunity appropriately.
  5. Enable early adopters to become enterprise data governance leaders and mentors.

How to Find the Best Interventions for Clinical Quality Improvement

How can health systems avoid just talking about improvement and instead achieve real progress in clinical quality performance? First, improvement teams need access to a robust data infrastructure that can provide a complete picture of performance. This analytic insight reveals process gaps and opportunity areas where the care team can target improvement efforts. After selecting an opportunity area, care teams are ready to follow the three-step process to achieve meaningful clinical improvement:

  1. The “why”: Identify the outcome goal.
  2. The “what”: Select a written, measurable, and time-sensitive process metric to evaluate the process.
  3. The “how”: Identify the best interventions that will support the desired change in a process.

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 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.

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.

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.

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.