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Data: Quality, Management, Governance

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Three Must-Haves for a Successful Healthcare Data Strategy

Healthcare is confronting rising costs, aging and growing populations, an increasing focus on population health, alternative payment models, and other challenges as the industry shifts from volume to value. These obstacles drive a growing need for more digitization, accompanied by a data-centric improvement strategy. To establish and maintain data as a primary strategy that guides clinical, financial, and operational transformation, organizations must have three systems in place:

  1. Best practices to identify target behaviors and practices.
  2. Analytics to accelerate improvement and identify gaps between best practices and analytic results.
  3. Adoption processes to outline the path to transformation.

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How to Build a Healthcare Data Quality Coalition to Optimize Decision Making

Healthcare data-informed decision making’s complexity and consequences demand the highest-quality data—a relationship that COVID-19 has amplified. Decision-making challenges associated with pandemic-driven urgency, variety of data, and a lack of resources have made it more critical than ever that organization’s build a data quality coalition and strategy to ensure systemwide data is fit for purpose. Having the people, processes, and technology necessary to define, evaluate, and monitor data quality allows for a quick, effective, and sustained response at an organizational scale. The coalition keeps all resources working together on the task at hand within a well-defined structure.

The Three Essential Responsibilities of a Nurse Informaticist

With data driving decisions at every level of a health system, healthcare organizations must have data experts who can understand and communicate the technological processes and the reasons behind them to clinical staff. Nurse informaticists bridge the gap between data and nursing practice by combining clinical experience and data expertise. They fulfill three pivotal responsibilities:

  1. Understand and communicate the “why” behind new processes.
  2. Implement new processes.
  3. Validate data quality.
With a nurse informaticist guiding data-driven processes, educating nurses, and validating data quality, health systems advance data beyond the data platform so it reaches the nursing workforce to inform decisions at the frontlines of healthcare delivery.

Why Data-Driven Healthcare Is the Best Defense Against COVID-19

COVID-19 has given data-driven healthcare the opportunity to prove its value on the national and global stages. Health systems, researchers, and policymakers have leveraged data to drive critical decisions from short-term emergency response to long-term recovery planning. Five areas of pandemic response and recovery stand out for their robust use of data and measurable impact on the course of the outbreak and the individuals and frontline providers at its center:

  1. Scaling the hospital command center to pandemic proportions.
  2. Meeting patient surge demands on hospital capacity.
  3. Controlling disease spread.
  4. Fueling global research.
  5. Responding to financial strain.

The Healthcare Cybersecurity Framework: A Top Defense Against Data Breaches and Attacks

Between 2017 and 2020, more than 93 percent of healthcare organizations experienced a data breach. While digital technology and connectivity is increasingly critical in meeting operational and clinical challenges, such as COVID-19, more integration also enables increased exposure to cyberattacks that can impact care delivery, safety, and privacy. In response to healthcare’s significant and growing cybersecurity threats, vendor organizations and their health system partners need a security framework. A defensible protocol holds vendors accountable to routine audits and compliance measures at a regular cadence, ensuring both parties keep cybersecurity programs active and optimized.

Healthcare Data Quality: Five Lessons Learned from COVID-19

Healthcare providers knew that COVID-19 would threaten the lives of their patients, but few understood the greater ripple effects across their business and industry as a whole. For providers, two significant COVID-19-induced challenges arose: analytic strain and resource limitations. These challenges highlighted the critical importance of data quality. Healthcare leaders can improve data quality throughout their organizations by understanding the data quality lessons learned from COVID-19. Five guidelines from these lessons will help organizations prepare for the next pandemic or significant analytic use case:

  1. Assess data quality throughout the pipeline.
  2. Do not leave analysts to firefight.
  3. Look outside the four walls of the organization.
  4. Data context and purpose matters.
  5. Use a singular vision to scale data quality.

Safeguarding the Ethics of AI in Healthcare: Three Best Practices

As artificial intelligence (AI) permeates the healthcare industry, analytics leaders must ensure that AI remains ethical and beneficial to all patient populations. In absence of a formal regulatory or governing body to enforce AI standards, it’s up to healthcare professionals to safeguard ethics in healthcare AI. The potential for AI’s use in support of the pandemic response can have enormous payoffs. However, ensuring its ethical implementation may prove challenging if healthcare professionals are not familiar with the accuracy and limitations of AI-generated recommendations. Understanding how data scientists calculate algorithms, what data they use, and how to interpret it is critical to using AI in a meaningful and ethical manner to improve care delivery. By adhering to best practices for healthcare AI, health systems can guard against bias, ensure patient privacy, and maximize efficiencies while assisting humanity.

Using Data to Ensure a Safe Return to School During COVID-19

With limited information about the novel coronavirus, industries are scrambling to create an effective response to more quickly and safely return to life before the pandemic. Data has proven to be the best way to capture information about the developing virus. With access to the latest, comprehensive COVID-19 data, decision makers in any industry—from education to healthcare—can develop a sustainable, viable approach to pandemic-era operations. In the education sector, leaders can use accurate, up-to-date COVID-19 data to make decisions about implementing in-person or virtual learning. When states across the country instituted virtual learning as a stopgap until it was safe to resume in-person education, the most vulnerable students experienced the greatest disadvantages. As these disparities grow with continued virtual learning, it is an imperative that leaders have access to the latest coronavirus data to rapidly return to face-to-face learning.

Medical Practices’ Survival Depends on Four Analytics Strategies

With limited resources compared to large healthcare organizations and fewer personnel to shoulder burdens like COVID-19, medical practices must find ways to deliver better care with less. Delivering quality care, especially in a pandemic, is challenging, but analytics insight can guide effective care delivery methods, especially for smaller practices. Comprehensive data combined with team members who can turn numbers into real-world information are essential for medical practices to ensure a strong financial, clinical, and operational future. Independent medical practices can rely on four analytics strategies to survive the uncertain healthcare market and plan for a sustainable future:

  1. Prioritize access to up-to-date, comprehensive data sources.
  2. Form a multidisciplinary approach to data governance.
  3. Translate data into analytics insight.
  4. Invest in analytics infrastructure to support rapid response.

Six Proven Methods to Combat COVID-19 with Real-World Analytics

As data in healthcare becomes more available than ever before, so does the need to apply that data to the unique challenges facing health systems, especially in a pandemic. Even with massive amounts of data, health systems still struggle to move data from spreadsheets to drive change in a clinical setting. These six methods allow health systems to transform data into real-world analytics, going beyond basic data usage and maximizing actionable insight:

  1. Create effective information displays.
  2. Add context to data.
  3. Ensure data processes are sustainable.
  4. Certify data quality.
  5. Provide systemwide access to data.
  6. Refine the approach to knowledge management.
Advancing data use in healthcare with real-world analytics arms health systems with effective tools to combat COVID-19 and continue delivering quality care driven by comprehensive, actionable insight.

How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework

Healthcare organizations increasingly understand the value of data quality, but many lack a systematic process for establishing and maintaining that quality. However, as COVID-19 response and recovery further underscores the need for timely, actionable data, organizations must take a more proactive approach to data quality. A structured process engages technical and subject matter expertise to define, evaluate, and monitor data quality throughout the pipeline. Health systems can follow a simple, four-level framework to measure and monitor data quality, ensuring that data is fit to drive quality data-informed decisions:

  1. Think of data as a product.
  2. Address structural data quality first.
  3. Define content level data quality with subject matter experts.
  4. Create a coalition for multidisciplinary support.

Population Health Success: Three Ways to Leverage Data

As the healthcare industry continues to focus on value, rather than volume, health systems are faced with delivering quality care to large populations with limited resources. To implement population health initiatives and deliver results, it is critical that care teams build population health strategies on actionable, up-to-date data. Health systems can better leverage data within population health and drive long-lasting change by implementing three small changes:

  1. Increase team members’ access to data.
  2. Support widespread data utilization.
  3. Implement one source of data truth.
Access to accurate, reliable data boosts population health efforts while maintaining cost and improving outcomes. With actionable analytics providing insight and guiding decisions, population health teams can drive real change within their patient populations.

Data Visualization Dashboards: Three Ways to Maximize Data

With an unpredictable future due to COVID-19, health systems must leverage data to drive decision making at every organizational level. Data visualization dashboards allow health systems to optimize their data and create a data-driven culture by displaying large, real-time data sets in an easy-to-understand dashboard. Health systems that rely on dashboard reporting maximize their data in three important ways:

  1. Time to value. Decision makers do not have time to wait for manually-created reports; dashboards quickly convey information so leaders can make swift decisions.
  2. Data democratization. Leveraging a central source of truth, dashboards allow leaders at every level to access the most updated, accurate data.
  3. Digestible data. Analysts can configure dashboards to highlight important figures and trends, so high-level leaders can understand complex data without diving into spreadsheets.

Self-Service Data Tools Unlock Healthcare’s Most Valuable Asset

Data is increasingly critical to the delivery of healthcare. However, due to its complexity and scope, frontline clinicians and other end users can’t always access the data they need when they need it. In addition, expectations for data at the point of care unduly burden data analysts, keeping them from advancing more sophisticated organizational analytics goals. In response to data productivity and efficiency challenges, self-service data solutions models only the high-value data, versus all available data, giving analysts and nontechnical users immediate and direct access to the data. These reusable models address three key challenges healthcare analytics programs face:

  1. Cost—avoid additional expense and labor of producing single-use models.
  2. Efficiency—save times associated with routinely producing new models.
  3. Maintenance—allow updates across the organization’s models, versus separate updates.

Achieve Data-Informed Healthcare in Eight Steps

Becoming a data-informed healthcare system starts with raw data and ends with meaningful change, driven by raw data. Health systems can follow an eight-step analytics ascension model to transform data into intelligence:

  1. Population Identification and Stratification
  2. Measurement
  3. Data
  4. Information
  5. Knowledge
  6. Insight
  7. Wisdom
  8. Action
Following the analytics ascension model allows improvement teams to avoid feeling overwhelmed, focus on each step, and see how each step fits into the overall objective, allowing health systems to maximize data.

Interoperability in Healthcare: Making the Most of FHIR

With the CMS and ONC March 2020 endorsement of HL7 FHIR R4, FHIR is positioned to grow from a niche application programming interface (API) standard to a common API framework. With broader adoption, FHIR promises to support expanding healthcare interoperability and prepare the industry for complex use cases by addressing significant challenges:

  1. Engaging consumers.
  2. Sharing data with modern standards.
  3. Building a solid foundation for healthcare interoperability.

The Biggest Barriers to Healthcare Interoperability

Improving healthcare interoperability is a top priority for health systems today. Fundamental problems around improving interoperability include standardization of terminology and normalization of data to those standards. And, the volume of data healthcare IT systems produce exacerbates these problems. While interoperability regulations focus on trying to make it easy to find and exchange patient data across multiple organizations and HIEs, the legislation’s lack of fine print and aggressive implementation timelines nearly ensures the proliferation of existing interoperability problems. This article discusses the biggest barriers to interoperability, possible solutions to interoperability problems, and why it matters.