Sepsis affects at least 1.7 million U.S. adults per year, making it a pivotal improvement opportunity for healthcare organizations. The condition, however, has proven problematic for health systems. Common challenges including differentiating between sepsis and a patient’s acute illness and data access. In response, organizations must have comprehensive, timely data and advanced analytics capabilities to understand sepsis within their populations and monitor care programs. These tools can help organizations identify sepsis, intervene early, save lives, and sustain improvements over time.
Along with the rest of the globe, 2021 healthcare trends across Asia-Pacific (APAC) countries will center on COVID-19 recovery and resuming the healthcare improvement journey. In the APAC region, however, a mix of developed and developing countries poses unique challenges, as healthcare access and basic infrastructure vary widely between urban and rural populations and economic levels. To shepherd healthcare out of the pandemic and enhance delivery overall in 2021, APAC nations will focus on increasing investment in digital health (including virtual care, machine learning, and EMR adoption), achieving universal health coverage, shifting more towards value, and improving payer-provider relationships.
Healthcare processes drive activities and outcomes across the health system, from emergency department admissions and procedures to billing and discharge. Furthermore, in the COVID-19 era’s uncertainty, process quality is an increasingly important driver in care delivery and organizational success. Given this broad scope of impact, process improvement is intrinsically linked to better outcomes and lower costs. Six strategies for healthcare process improvement illustrate the roles of strategy, skillsets, culture, and advanced analytics in healthcare’s continuing mission of transformation.
With preventable patient harm associated with over 400,000 deaths in the U.S. annually, improving safety is a top priority for healthcare organizations. To reduce risks for hospitalized patients, health systems are using patient safety analytics and trigger-based surveillance tools to better understand and recognize the types of harm occurring at their facilities and intervene as early as possible.
Six examples of analytics-driven patient safety success cover improvement in the following areas:
Wrong-patient order errors.
Clostridioides difficile (C. diff).
The traditional sepsis identification method—based on a combination of physician notes, coding, and billing—is often varied and too subjective, leading to inaccurate data. Because margins are tight and health systems can’t afford to waste any resources, clinical teams need to start with the most effective sepsis identification method. Using physiological data, such as vital signs, to identify sepsis is proving to be highly effective.
With the physiological data approach, providers rely on the body’s response—rather than being steered by biases, anecdotal information, or reimbursement rates—to more accurately identify patients with sepsis. With a more effective approach to sepsis identification, providers can implement interventions sooner, leading to better outcomes.
Effective, sustainable healthcare transformation rests in the organizational operations that power care delivery. Operations include the administrative, financial, legal, and clinical activities that keep health systems running and caring for patients. With operations so critical to care delivery, forward-thinking organizations continuously strive to improve their operational outcomes. Health systems can follow thought leadership that addresses common industry challenges—including waste reduction, obstacles in process change, limited hospital capacity, and complex project management—to inform their operational improvement strategies.
Five top insights address the following aspects of healthcare operational outcomes improvement:
Quality improvement as a foundational business strategy.
Using improvement science for true change.
Increasing hospital capacity without construction.
Leveraging project management techniques.
Features of highly effective improvement projects.
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.
With the right approach, an improvement team can measure the results and know if the changes they made will actually lead to the desired impact.
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.
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.
A healthcare CIO’s role can demand such an intense focus on technology that IT leaders may struggle to find natural opportunities to engage with their C-suite peers in non-technical conversations. To bridge the gap, healthcare CIOs can answer five fundamental questions to better align their programs with organizational strategic goals and guide IT services to their full potential:
Whom do we serve?
What services do we provide?
How do we know we are doing a great job?
How do we provide the services?
How do we organize?
According to a November 2019 survey, 62 percent of clinicians and other healthcare professionals view burnout as a major problem industrywide. When asked for the best way to address clinician burnout problems, the most popular solution was less-complex workflows, which is the aim of emerging point-of-care analytics solutions.
Responses to additional questions reveal more about clinician burnout experience and views on the technology designed to help:
At your organization, how big of a problem is clinician burnout?
What is the best way to solve clinician burnout problems?
What are the biggest barriers to adopting closed-loop, point-of-care analytics capabilities at your organization
What are the biggest problems arising from a lack of adopting closed-loop, point-of-care analytics capabilities?
Waste is a $3 trillion problem in the U.S. Fortunately, quality improvement theory (per W. Edwards Deming) intrinsically links high-quality care with financial performance and waste reduction. According to Deming, better outcomes eliminate waste, thereby reducing costs.
To improve quality and process and ultimately financial performance, an industry must first determine where it falls short of its theoretic potential. Healthcare fails in five critical areas:
Massive variation in clinical practices.
High rates of inappropriate care.
Unacceptable rates of preventable care-associated patient injury and death.
A striking inability to “do what we know works.”
Huge amounts of waste.
Patient comments such as “I feel dizzy” or “my stomach hurts” can tell clinicians a lot about an individual’s health, as can additional background, including zip code, employment status, access to transportation, and more. This critical information, however, is captured as free text, or unstructured data, making it impossible for traditional analytics to leverage.
Machine learning tools (e.g., NLP and text mining) help health systems better understand the patient and their circumstances by unlocking valuable insights residing unstructured data:
NLP analyzes large amounts of natural language data for human users.
Text mining derives value through the analysis of mass amounts of text (e.g., word frequency, length of words, etc.).
Patients who undergo surgery frequently follow a rehabilitation program afterwards to promote recovery. However, starting this program before the procedure may help further accelerate recovery time. Prehabilitation is defined as physical or lifestyle preparation that happens before surgery and is designed to help patients regain function in less time.
Prehabilitation includes the following four main components:
Medical optimization of pre-existing medical conditions.
Providing coordinated care from the pre-surgery period to post-operative recovery helps ensure the best patient outcomes. Additionally, health systems can glean important insights about best practices when they effectively follow the patient journey and capture relevant data throughout.
As health systems face more pressure than ever to deliver cost savings, they’re turning their attention to cost-per-case improvement projects. These strategies can produce quick wins for improvement teams looking to gain momentum and buy-in. This article addresses the following topics:
How to identify areas of opportunity.
The importance of costing accuracy.
Four strategies for implementing cost-per-case improvement projects.
Example projects for new teams.
How to sustain results.
While healthcare waits for the expanded data interoperability that FHIR promises, the industry needs an immediate solution for accessing and using disparate data from across the continuum of care. With FHIR potentially several years away, continuity of care documents (CCDs) are the best option for acquiring the ambulatory clinical care data health systems need to close quality gaps today. Because organizations that rely only on claims data to drive quality improvement risk missing out on more that 80 percent of patient information, CCDs are the current must-have answer to interoperability for successful quality improvement.
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.
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.
Quality improvement efforts are more important than ever. However, even improvement efforts that have the right people, processes, and technology can struggle to make progress. A medical writer with healthcare knowledge and strong information design skills may be the missing ingredient that can help speed time to adoption and value.
This article discusses the functions a medical writer can fulfill, and why they matter. You will also learn:
The four skills that a medical writer with strong information design skills brings to an improvement team.
Examples of output of medical writers in a healthcare setting.
The skills a medical writer needs.
Additionally, you will learn how to find this unique skill set and where you might find this key person.
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.
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.
A robust data analytics operation is necessary for healthcare systems’ survival. Just like any business, the analytics enterprise needs to be well managed using the principles of successful business operations.
This article walks through how to run an analytics operation like a business using the following five-question framework:
Who does the analytics team serve and what are those customers trying to do?
What services does the analytics team provide to help customers accomplish their goals?
How does the analytics team know they’re doing a great job and how do they communicate that effectively to the leadership team?
What is the most efficient way to provide analytics services?
What is the most effective way to organize?
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.
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.