The year 2020 marks a decade since the passage of the Affordable Care Act in 2010 and healthcare’s first transitional steps from volume to value. The 10-year progress report is mixed. On one hand, CMS’s emphasis on quality and cost is driving an upward trend for patients and providers, with substantial improvement in readmissions; on the other hand, organizations still need to simplify and consolidate value-based programs for more widespread positive impact. As the industry enters into another decade of value, it’s time for health systems to consider the impacts of these programs so far and make sure they have the processes and tools in place to succeed in an increasingly value-driven industry.
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Social distancing, effective hand-washing techniques, sneezing into elbows, and the like are critical means of mitigating the spread and impact of COVID-19, but the pandemic has also prompted another area of concern: cybersecurity. A growing remote workforce, more collective time online, and increasingly frequent social engineering attacks that take advantage of public curiosity about and fear of the novel coronavirus are exposing system and network vulnerabilities. Remote workers can increase their online safety by refreshing and ramping up cyber-hygiene best practices, including learning to recognize and report suspicious emails and protecting home internet connections.
Community Health Network identified that inconsistent oversight of durable medical equipment (DME), and process variation, were a likely source of waste and lost revenue. The health network sought a systemwide, data-driven process for the purchasing, dispensing, and billing of DME. A data platform and analytics applications were utilized to understand organizational performance, identify opportunities for improvement, and evaluate the impact of these changes on patient, financial, and organizational outcomes.
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:
- 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.
- 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.
As health systems across the country scramble to respond to COVID-19, we've pulled together news and resources to help organizations and professionals prepare. In this week's news roundup: steps healthcare facilities can take now to prepare for COVID-19, guidance from the CDC on preparing for community transmission, COVID-19 maps and visuals from the Center for Infectious Disease Research and Policy, and much more.
While most consumer-oriented industries have turned to mobile-first, cloud-based platforms for consumer interaction, healthcare lags behind in digitization, particularly when it comes to self-service consumer engagement. As digital consumer interaction increasingly drives enterprise success, healthcare must join the modern digital playing field. To get there, organizations need to establish digital investment and enablement frameworks and can then follow five strategies for stable, scalable transformation:
- Formally define “digital” for the organization.
- Follow 10 guiding principles to support digital.
- Divide technology into appropriate portfolios.
- Develop an analogy to explain the integrated portfolio approach.
- Strategically select vendor partners.
Community Health Network, a hospital system in Indiana, discovered that its hospital-acquired C. diff infection (HA-CDI) rate was higher than the national benchmark. The organization knew it needed to decrease infection rates, but without timely, meaningful data, leaders couldn’t identify the right areas to focus improvement efforts. With the use of a high-level, robust analytics system that allowed better access to data, team members were able to determine where to focus their efforts.
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.
Health systems alike struggle to effectively manage hospital patient flow. With machine learning and predictive models, health systems can improve patient flow for different departments throughout the system like the emergency department. Health systems should focus on three key areas to foster successful data science that will lead to improved hospital patient flow: Key 1. Build a data science team. Key 2. Create a ML pipeline to aggregate all data sources. Key 3. Form a comprehensive leadership team to govern data. Improving hospital patient flow through predictive models results in reduced patient wait times, reduced staff overtime, improved patient outcomes, and improved patient and clinician satisfaction.
As healthcare organizations strive to provide better care for patients, they must have an effective clinical measurement system to monitor their progress. First, there are only two potential aims when designing a clinical measurement system: measurement for selection or measurement for improvement. Understanding the difference between these two aims, as well as the connection between clinical measurement and improvement, is crucial to designing an effective system. This article walks through the distinct difference between these two aims as well as how to avoid the common pitfalls that come with clinical measurement. It also discusses how to identify and track the right data elements using a seven-step process.
As the transition of healthcare payment models from volume to value takes longer than expected, healthcare organizations are balancing fee for service (FFS) with value-based care (VBC). In this week's news roundup: the role of Accountable Care Organizations (ACOs) in VBC; ten strategies for navigating the shift from volume to value; and where providers and payers stand on consumerism, value-based care, and healthcare transformation.
Health system mergers can promise significant savings for participating organizations. Research, however, indicates as much as a tenfold gap between expectation and reality, with systems looking for a savings of 15 percent but more likely to realize savings around 1.5 percent. Driving the merger expectation-reality disparity is a complex process that, without diligent preparation and strategy, makes it difficult for organizations to fully leverage cost synergies. With the right framework, however, health systems can achieve the process management, data sharing, and governance structure to align leadership, clinicians, and all stakeholders around merger goals.
MultiCare Health System’s Pulse Heart Institute (Pulse Heart) recognized that better care coordination was required for patients receiving cardiac, thoracic, and vascular care. The organization wanted to further improve quality outcomes, provider engagement and recruitment, and its own economic health. To meet these objectives, Pulse Heart focuses on clinician engagement and organizational alignment, ensuring widespread access to meaningful, actionable data and analytics to inform decisions and drive improvement.
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:
- Best practices to identify target behaviors and practices.
- Analytics to accelerate improvement and identify gaps between best practices and analytic results.
- Adoption processes to outline the path to transformation.
During this webinar, you’ll learn the following:
- How to engage leaders up front with the goal of operationalizing analytics.
- What types of machine learning methods best support operationalizing analytics.
- How to operationalize machine learning-driven results to improve patient flow.
The journey for healthcare organizations to become data driven is complex but absolutely critical for success in today's increasingly digitized environment. In this week's news roundup: how data literacy plays a key role in becoming a data-driven healthcare organization; how the lack of data literacy costs employers five days of productivity per year; why data storytellers will define the next decade of data; and more.
As the transition of healthcare payment models from volume to value takes longer than expected, healthcare organizations must balance fee for service (FFS) with value-based care (VBC). The transition to VBC will accelerate, but as FFS persists and still generates adequate margins, organizations must also continue to be successful under volume-based reimbursement. Ten tools can help health systems balance VBC with FFS:
- A member perspective.
- Cautious investment in hard delivery assets.
- Accelerated investment in digital infrastructure.
- Innovative digital engagement solutions.
- Pricing concessions.
- Aligned incentives.
- Network management.
- Payer-provider trust and collaboration.
- Clinician and administrative alignment.
- Physician leadership and accountability.
This healthcare organization, comprised of a specialty hospital and multiple clinics, sought to improve safety for its patients, focusing on identifying wrong-patient order errors. To better understand and improve patient safety, the organization needed to move beyond passive surveillance. By using multiple detection methods for identifying wrong-patient errors and establishing triggers that identify when a wrong-patient order may have occurred, hospital and clinic staff are able to investigate instances.
After a tumultuous 2019, healthcare organizations are pivoting to make sense of the latest changes and prepare to face the top 2020 healthcare trends:
- Consumerism—Can health systems respond to the consumer demands of better access and price transparency?
- Financial Performance—With mergers, acquisitions, and private sector companies entering the healthcare arena, how will traditional hospitals and clinics compete?
- Social Issues—How will health organizations respond to the opioid crisis and consider social determinants of health as part of the care process to provide comprehensive treatment?
With improvement science combined with analytics, health systems can better understand how, as they implement new process changes, to use theory to guide their practice, and which improvement strategy will help increase the likelihood of success. The 8-Step Improvement Model is a framework that health systems can follow to effectively apply improvement science:
- Analyze the opportunity for improvement and define the problem.
- Scope the opportunity and set SMART goals.
- Explore root causes and set SMART process aims.
- Design interventions and plan initial implementation.
- Implement interventions and measure results.
- Monitor, adjust, and continually learn.
- Diffuse and sustain.
- Communicate Quantitative and Qualitative Results.