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.
Clinical Analytics and Decision Support
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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:
- Identifies rising-risk patients.
- Uses a specific social determinant assessment to capture factors beyond claims data.
- Integrates EMR data to achieve quality measures.
- Identifies patients for palliative or hospice care.
- Identifies patients with chronic conditions.
- Increases patient engagement.
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.
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.
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 Decision Support Helps CFOs Achieve Their Top Goal: Timely, Accurate, Agile Decision Making
Supporting decision making is a top goal for CFOs today, according to a 2017 Kaufmann Hall CFO survey. Healthcare decision support empowers CFOs and their finance teams to make accurate, agile, and timely decisions, from rolling forecasts of future trends to risk-adjusted scenario modeling. In addition to helping CFOs make good decisions, healthcare decision support helps CFOs lead their teams and organizations improve in four key ways:
- Data-driven growth and practice expansion.
- Improved ability to negotiate favorable risk-based contracts with payers.
- Effectively and fairly address important physician compensation issues.
- Improve population health management.
It’s no easy task to lead a real-time, outcomes-focused, high-performing health system. That’s why every chief medical officer (CMO) needs a healthcare executive dashboard—a decision support tool that helps these senior physician leaders ensure their organizations continue to achieve the seven key attributes of a high-performing health system:
- Efficient provision of services.
- Organized system of care.
- Quality measurement and improvement activities.
- Care coordination.
- Use of information technology and evidence-based medicine.
- Compensation practices that promote the above-listed objectives.
Healthcare CEOs and other C-Suite leaders can’t make quality decisions in today’s rapidly changing, complex environment without decision support. Healthcare CEOs are starting to realize that executive dashboards with personally tailored views of key metrics are no longer a luxury, but an absolute necessity, for three key reasons:
- Helps leaders analyze and digest large amounts of data relating to care quality, operations, contracting, and major purchasing decisions.
- Gives leaders a clear understanding of the financial aspects of their systems, such as revenue streams, cost drivers, costs of capital, bundled payments, and payment reforms.
- Facilitates conflict resolution and helps leaders work collaboratively—using a matrix management approach—with peers, direct reports, and system experts.
Leading Wisely in Healthcare: Why the Next Generation Executive Decision Support System is an Industrywide Imperative
Healthcare leaders are struggling to make effective, data-driven decisions given the industry’s unexpected, complex, and rapidly changing challenges, from advancing healthcare reform to rising consumerism. Fortunately, there’s hope with the next generation executive decision support system, which facilitates decision making in several key ways:
- Aggregates reliable, up-to-date information from all available sources, and makes it readily accessible.
- Enables leaders to break information down and view it in more user-friendly ways—often in the form of graphs that make important conclusions or trends more recognizable and understandable.
- Supports a leader’s ability to drill down into the data in search of problems’ root causes.
- Plays an important communication and collaboration role, helping leaders work with the intellectual assets of the organization to problem solve and align the organization around a common vision and strategy.