Accountable Care Organizations (ACOs) and clinically integrated networks (CINs) are two types of organizations working to address the problem of rising costs. As ACOs and CINs continue to evolve, organizations moving into value-based care (VBC) face an ever-changing landscape. This article looks at the evolution of the ACO and CIN models, what new tools ACOs employ today to promote success, and lessons learned from organizations that have succeeded in alternative payment models. It also explores what healthcare experts believe the future of alternative payment models will look like and competencies to develop to meet those changing demands.
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In order to thrive in an increasingly challenging healthcare environment, undertaking quality improvement projects is more important than ever for healthcare systems’ continued survival. However, health systems need to tackle the right projects at the right time to maximize the impact to their organization.
This article shares both clinical and financial and operational examples of quality improvement in healthcare that may help others as they tackle improvement projects. Some examples shared include:
Pharmacist-led Medication Therapy Management (MTM) reduces total cost of care.
Optimizing sepsis care improves early recognition and outcomes.
Boosting readiness and change competencies successfully reduces clinical variation.
New generation Activity-Based Costing (ABC) accelerates timeliness of decision support.
Systematic, data-driven approach lowers length of stay (LOS) and improves care coordination.
Clinical and financial partnership reduces denials and write-offs by more than $3 million.
In today’s challenging environment, healthcare leaders must seek opportunities to boost revenue through improved financial performance and reimbursement. Some common strategies include reducing the number of outstanding bill hold accounts, reducing A/R days, and managing discharged not final billed (DNFB) cases.
This article tackles, the following topics:
Common reasons accounts remain unbilled.
Identifying opportunities for improvement.
Using data analytics and process improvement to achieve financial goals.
Creating lasting improvements.
A hot topic in healthcare right now, especially in the medical coding world is the Hierarchical Condition Category (HCC) risk adjustment model and how accurate coding affects healthcare organizations’ reimbursement.
With almost one third of Medicare beneficiaries enrolled in Medicare Advantage plans, it’s more important than ever for healthcare organizations to pay attention to this model and make sure physicians are coding diagnoses appropriately to ensure fair compensation. This article walks through basics of the risk adjustment model, why coding accuracy is so important, and five action items for interdisciplinary work groups to take. They include:
Having an accurate problem list.
Ensuring patients are seen in each calendar year.
Improving decision support and EMR optimization.
Widespread education and communication.
Tracking performance and identifying opportunities.
While healthcare data is an ever-growing resource, thanks to broader EHR adoption and new sources (e.g., patient-generated data), many health systems aren’t currently leveraging this information cache to its full potential. Analysts can’t extract and analyze a significant portion of healthcare data (e.g., follow-up appointments, vitals, charges, orders, encounters, and symptoms) because it’s in an unstructured, or text, form, which is bigger and more complex than structured data.
Natural language processing (NLP) taps into the potential of unstructured data by using artificial intelligence (AI) to extract and analyze meaningful insights from the estimated 80 percent of health data that exists in text form. Though still an evolving capability, NLP is showing promise in helping organizations get more from their data.
Opportunity analysis uses data to identify potential improvement initiatives and quantifies the value of these initiatives—both in terms of patient care benefits and financial impact. This process is an effective way to find unwarranted and costly clinical variation and, in turn, develop strategies to reduce it, improving outcomes and saving costs along the way. Standardizing the opportunity analysis process makes it repeatable and prioritizes actionable opportunities.
Quarterly opportunity analysis should follow four steps:
Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm.
Engaging with clinicians to identify opportunities and, in the process, get clinician buy in.
Digging deeper into the suggested opportunities to prioritize those that offer the greatest benefits.
Presenting findings to the decision makers.
Bobbi Brown, MBA, and Stephen Grossbart, PhD have analyzed the biggest changes in the healthcare industry and 2018 and forecasted the trends to watch for in 2019. This report, based on their January 2019, covers the biggest 2019 healthcare trends, including the following:
The business of healthcare including new market entrants, business models and shifting strategies to stay competitive.
Increased consumer demand for more transparency
Continuous quality and cost control monitoring across populations.
CMS proposals to push ACOs into two-sided risk models.
Fewer process measures but more quality outcomes scrutiny for providers.
Customer journey analytics uses machine learning and big data to track and analyze when and through what channels customers interact with an organization, with an aim to influence behavior (e.g., buying behaviors among retail customers). Similarly, healthcare organizations want to influence health-related behaviors, such a taking medication as prescribed and not smoking, to improve outcomes and lower the cost of care. In a partnership with an analytics services provider, a payer organization is leveraging customer journey analytics among healthcare consumers to identify the best opportunities and channels for patient outreach. With this analytics-driven engagement strategy, the payer has found an opportunity to significantly improve patient engagement—a predicted overall increase from 18 percent to 31 percent.
Health systems have vast amounts of data, but frequently struggle to use that data to solve strategic problems in a timely fashion. A healthcare analytics team, made up of the right people with the right tools and skillsets, can help address these challenges. This article walks through the steps organizations need to take to put an effective analytics team in place.
These include the following:
Recognizing the need for change.
Demonstrating the value of an analytics team.
Conducting a current state assessment.
Implementing a phased approach.
Building a roadmap.
Making the pitch.
Putting the roadmap into action.
The article also includes the foundation skills to look for when putting together the team and tips on how best to organize.
Health systems are challenged by the need to keep patients and employees satisfied and engaged. This can be especially difficult for organizations in flux, growing, merging, and changing. And as leaders of these organizations know, poor patient satisfaction ratings lead to reduced reimbursements, which affect the bottom line.
To meet this challenge and improve patient satisfaction, health system leaders are taking advantage of technology, such as rounding software, that supports effective communication and drives the type of culture change that boosts both caregiver and patient satisfaction and encourages engagement. Embedding rounding technology into current processes makes rounding better and easier. The correlation between effective, efficient rounding and high patient satisfaction scores is clear. Rounding can and does increase engagement and satisfaction, which in turn leads to higher reimbursement potential. Learn how health system leaders can move from talking about rounding technology to incorporating it into daily workflow.
Health systems attempt to measure an ever-increasing amount of clinical measures, these often miss the mark of what matters to patients. Patient-Reported Outcomes (PROs) are the missing link in empowering patients and helping to define good outcomes. This article walks through how patient-reported outcome measures (PROMs) can help identify best practices and drive system-wide quality improvement. PROMs can help health systems do the following:
Serve as a guide for appropriateness and efficiency.
Lead to better shared decision-making.
Demonstrate value and transparency
This article also discusses the effect of PROMs on providers in a culture of “one more thing,” and tips for effective implementation.
Patient safety is a top concern for healthcare organizations. Fortunately, health IT assists leadership and frontline clinicians in the ongoing effort to improve patient care. This e-book comprises ten articles outlining the intersection of technology and patient care, highlighting how organizations can implement patient safety best practices.
With an increasing emphasis on value-based care, Accountable Care Organizations (ACOs) are here to stay. In an ACO, healthcare providers and hospitals come together with the shared goals of reducing costs and increasing patient satisfaction by providing high-quality coordinated healthcare to Medicare patients.
However, many ACOs lack direction and experience difficulty understanding how to use data to improve care. Implementing a robust data analytics system to automate the process of data gathering and analysis as well as aligning data with ACO quality reporting measures.
The article walks through four keys to effectively implementing technology for ACO success:
Build a data repository with an analytics platform.
Bring data to the point of care.
Analyze claims data, identify outliers, including successes and failures.
Combine clinical claims, and quality data to identify opportunities for improvement.
Many health systems have a hospital capacity problem as demand for patient beds rises. When the supply of usable patient beds can’t meet demand, the negative impact on patients and staff can be significant.
Hospitals can solve capacity problems with four key concepts:
Using data, start with the problem and the ideal solution.
Be sure the analytics team works with teams throughout the organization—including leadership.
Have leaders spend time with the operations team to understand workflow.
Focus on the impact, not the tool.
Healthcare today is in the midst of a massive transformation. The opportunities for improvement are great if healthcare systems can do the following:
Reduce clinical variation.
Reduce rates of inappropriate care and care-associated patient injury and death.
Follow accepted best care practices.
This article covers the different types of waste in healthcare systems, ways to reduce them, financial alignment around waste reduction opportunities, and the importance of reducing clinical variation. The core driver of healthcare systems must be improving clinical quality. Almost always, with proper clinical management, better care is cheaper care through waste management.
As healthcare systems are pressured to cut costs and still provide high-quality care, they will need to look across the care continuum for answers, reduce variation in care, and look to emerging technologies. This article walks through how to evaluate the safety and effectiveness and of emerging healthcare technology and prioritize high-impact improvement projects using a robust data analytics platform. Topics covered include:
The importance of identifying variation in innovation.
Ways to improve outcomes and decrease costs.
The value of an analytics platform.
The reliable information that produce sparks for innovation.
Identifying and evaluating emerging healthcare technology.
Knowing what data to use.
The difference between efficacy and effectiveness in evaluation of emerging healthcare technology.
Health systems continue to prioritize reducing hospital readmissions as part of their value-based payment and population health strategies. But organizations that aren’t fully integrating analytics into their readmission reduction workflows struggle to meet improvement goals. By embedding predictive models across the continuum of care, versus isolated them in episodes of care, health systems can leverage analytics for meaningful improvement.
Organizations that integrate predictive models into readmissions reduction workflows have achieved as much as a 40 percent reduction in risk-adjusted readmissions indexes. Effective analytics integration strategies use a multidisciplinary development approach to meet the needs of a patient’s entire care team and deliver common tools for all involved in the patient’s healthcare journey.
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.
Social determinants of health (SDOH) data captures impacts on patient health beyond the healthcare delivery system. Traditional health data (e.g., from healthcare encounters) only tells a portion of the patient and population health story. To understand the full spectrum of health impacts (e.g., from environment to relationship and employment status), organizations need data from their patient’s daily lives. The urgency for SDOH data is particularly strong today, as value-based payment increasingly presses health systems to raise quality and lower cost. Without fuller insight into patient health (what happens beyond healthcare encounters) organizations can’t align with community services to help patients meet needs of daily living—prerequisites for maintaining good health.
Standardizing SDOH data into healthcare workflows, however, requires an informed strategy. Health systems will benefit by following a standardization protocol that includes relevant and comprehensive domains, engages patients, enables broader understanding of patient health, integrates with organizational EHRs, and is easy for clinicians to follow.
Current quality measures are expensive and time consuming to report, and they don’t necessarily improve care. Many health systems are looking for better ways to measure the quality of their care, and they are using data analytics to achieve this goal. Data analytics can be helpful with quality improvement. There are four key considerations to evaluate quality measures:
Organizations must develop measures that are more clinically relevant and better represent the care provided.
Clinician buy-in is critical. Without it, quality improvement initiatives are less likely to succeed.
Investment in tools and effort surrounding improvement work must increase. Tools should include data analytics.
Measure improvement must translate to improvement in the care being measured.
When the right measures are in place to drive healthcare improvement, patient care and outcomes can and do improve.
While many industries are leveraging digital transformation to accelerate their productivity and quality, healthcare ranks among the least digitized sectors. Healthcare data is largely incomplete when it comes to fully representing a patient’s health and doesn’t adequately support diagnoses and treatment, risk prediction, and long-term health care plans. But even with the obvious urgency for increased healthcare digitization, the industry must raise this trajectory with sensitivity to the impacts on clinicians and patients. The right digital strategy will not only aim for more comprehensive information on patient health, but also leverage data to empower and engage the people involved.
Health systems can follow five guidelines to digitize in a sustainable, impactful way:
Achieve and maintain clinician and patient engagement.
Adopt a modern commercial digital platform.
Digitize the assets (the patients) and the processes.
Understand the importance of data to drive AI insights.
Prioritize data volume.
Drs. Allen Frankel and Michael Leonard have developed a framework for creating high-reliability organizations in healthcare. This report, based on their 2018 webinar, covers the components and factors of this frame work, including:
Improvement and Measurement
Teamwork and Communication
Twenty years after Intermountain Healthcare launched its enterprise data warehouse in 1998, industry leaders are looking at what they did right, what they’d do differently, and what the future holds for healthcare data and analytics. While early successes (such as a hiring framework of social, domain, and technical skills; lightweight data governance; and late-binding architecture) continue to hold their value, advanced analytics and technology and innovation in diagnosis and treatment are reshaping the capabilities of and demands on the healthcare data warehouse. Present-day and future healthcare IT leaders will have to revisit approaches to data warehousing people, processes, and technology to understand how they can improve, continue to adapt, and fully leverage emerging opportunities.
Data is everywhere. But without a plan to extract meaning from data and turn insights into action, data can’t impact outcomes. Generating value from data takes work, but it can be done. To create compelling data insights that promote action, health systems can follow three guiding principles for actionable healthcare data analytics as well as hire analysts with seven important skills.
Three principles form the foundation for actionable healthcare data analytics:
Hire generalists over specialists.
Develop a team that’s highly aligned and loosely coupled.
Based on a 2018 Healthcare Analytics Summit presentation, this report details the four phases necessary for successful healthcare data governance:
Elevate a vision and agenda that align with organizational priorities.
Establish an organizational structure to fulfill the data governance mandate.
Execute with prioritized data governance projects, people and resource assignment, and disciplined focus on the work.
Extend data governance investments and efforts through established practices.
Each step must follow the core principles of stakeholder engagement, shared understanding, alignment, and focus. Effective healthcare data governance is not a one-time event and requires ongoing and iterative efforts.