Most health systems suffer from data clutter and efficient problems. As a result, analysts spend most of their time searching for data, not performing high value work. There are three steps that can help you address your data management issues: 1) find all your dispersed analysts in the organization, 2) assess your analytics risks and challenges, 3) champion the creation of an EDW as the foundation for clinical data management.
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The role of a data lake in healthcare analytics is essential in that it creates broad data access and usability across the enterprise. It has symbiotic relationships with an enterprise data warehouse and a data operating system. To avoid turning the data lake into a black lagoon, it should feature four specific zones that optimize the analytics experience for multiple user groups:
- Raw data zone.
- Refined data zone.
- Trusted data zone.
- Sandbox data zone.
When expenses exceed revenue, business has a financial problem. In healthcare, the focus has been on revenue for so long, we’ve lost sight of runaway costs brought about by high labor and technology expenses, inefficient use of resources, and supply waste. Recognizing the cost problem is a big first step toward solving it. Five expense-controlling strategies can play a significant role in returning healthcare systems to a stronger financial position:
- Refocus on labor management.
- Manage employed physicians.
- Change the patient encounter environment.
- Augment standard approaches with technology.
- Manage patient access and flow through the healthcare system.
Besides improving your information systems and educating your staff on the ins and outs of managing revenue, there are many more opportunities for improvement. Here are five suggestions to help health systems improve their revenue cycle management:
- Trend and benchmark your healthcare data.
- Use DOS to Mine Your Healthcare Data.
- Constantly ask frontline staff for suggestions.
- Monitor all payer contracts.
- Maintain convenient and caring touch points with patients.
Healthcare organizations seeking to achieve the Quadruple Aim (enhancing patient experience, improving population health, reducing costs, and reducing clinician and staff burnout), will reach their goals by building a rich analytics ecosystem. This environment promotes synergy between technology and highly skilled analysts and relies on full interoperability, allowing people to derive the right knowledge to transform healthcare. Five important parts make up the healthcare analytics ecosystem:
- Must-have tools.
- People and their skills.
- Reactive, descriptive, and prescriptive analytics.
- Matching technical skills to analytics work streams.
With an estimated 80 percent of medical errors resulting from miscommunication among healthcare teams, organizations can significantly improve outcomes with better communication. A communication methodology outlines the essential information clinicians need to share, giving care teams the knowledge they need, when they need it, to make informed treatment decisions. One communication toolkit, SBAR (Situation, Background, Assessment, Recommendation), defines the essential information clinicians must share when they hand off patient care from the inpatient to the ambulatory setting:
- S (situation): The patient’s current situation.
- B (background): Information about the current situation.
- A (assessment): Assessment of the situation and background and potential treatment options.
- R (recommendation): Recommended action.
Those in Big Data and Healthcare Analytics circles will seldom hear the phrase “less is more.” In a clinical setting however, there is an important lesson to learn in regards to the effective execution of predictive analytics. We should not confuse more data with more insight. More data is simply more—as in more tables, more lists, more replicates, more clinics, more controls, more rows, tables of tables and lists of lists, etc. You get the idea. In short, for predictive analytics to be effective in a clinical venue, a specific focus will always trump global utility.
More people in the U.S. die from sepsis than from prostate cancer, breast cancer, and AIDS…combined. Although health systems continue working to improve outcomes for septic patients, there is tremendous room for improvement. Preparing health systems to most effectively tackle sepsis starts with an awareness of consensus definitions of sepsis and continues with following evidence-based recommendations from credible organizations, such as the Surviving Sepsis Campaign and the Sepsis Alliance. Distilling ever-evolving recommendations and best practices for sepsis is time intensive. This article facilitates healthcare’s distillation effort by highlighting the five key areas health systems can target to improve sepsis outcomes (based on evidence-based guidelines and Health Catalyst’s first-hand experience with healthcare partners):
- Early ED recognition
- Three-hour sepsis bundle compliance
- Six-hour sepsis bundle compliance
- In-house recognition of sepsis
- Sepsis readmissions: prioritize risk stratification
Dr. John Haughom explains 5 key Deming processes that can be applied to healthcare process improvement. These include 1) quality improvement as the science of process management, 2) if you cannot measure it, you cannot improve it, 3) managed care means managing the processes of care (not managing physicians and nurses), 4) the importance of the right data in the right format at the right time in the right hands, and 5) engaging the “smart cogs” of healthcare.
The opioid-related death rate in the U.S. has quadrupled since 1999, making more effective ways to predict opioid misuse a healthcare priority. A new generation of machine learning-enabled risk assessment tools promises to deliver broader and more relevant insight into a patient’s risk. With more comprehensive insight (including comorbidities, other substance abuse, the amount of medication prescribed, and the duration of opioid use), clinicians can make informed decisions when prescribing opioids and reduce the risk that patients will misuse, abuse, or overuse the pain killers. Clinicians will also be able to identify which patients might benefit from alternatives to opioid pain management (non-pharmacologic, multi-modal therapies, or care management programs).