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Improving Strategic Engagement for Healthcare CIOs with Five Key Questions

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:

  1. Whom do we serve?
  2. What services do we provide?
  3. How do we know we are doing a great job?
  4. How do we provide the services?
  5. How do we organize?

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Closed-Loop Analytics Approach: Making Healthcare Data Actionable

Healthcare organizations rely on data to support informed decisions. To be truly valuable, data must be high quality and meet two criteria for end-users:

  1. Data must be transformed from its raw, obscure form into actionable insights.
  2. Data-driven insights must be immediately accessible at the point of care (versus in static dashboards or buried on the intranet).
Closed-Loop Analytics™ methodology transforms raw data into actionable, accessible insight—providing physicians and nurses with critical insight into their patients’ situation and how they can effectively intervene. A Closed-Loop Analytics approach will become increasingly essential as healthcare becomes more systems dependent.

How to Use Text Analytics in Healthcare to Improve Outcomes—Why You Need More than NLP

Given the fact that up to 80 percent of clinical data is stored in unstructured text, healthcare organizations need to harness the power of text analytics. But, surprisingly, less than five percent of health systems use it due to resource limitations and the complexity of text analytics. But given the industry’s necessity to use text analytics to create precise patient registries, enhance their understanding of high-risk patient populations, and improve outcomes, this executive report explains why systems must start using it—and explains how to get started. Health systems can start using text analytics to improve outcomes by focusing on four key components:

  1. Optimize text search (display, medical terminologies, and context).
  2. Enhance context and extract values with an NLP pipeline.
  3. Always validate the algorithm.
  4. Focus on interoperability and integration using a Late-Binding approach.
This broad approach with position health systems for clinical and financial success.

Healthcare Information Systems: A Look at the Past, Present, and Future

Healthcare information systems are integral to hospital operations and clinical care for patients. In the 1960s healthcare was driven by Medicare and Medicaid and HIT developed shared hospital accounting systems. In the 1970s communication between departments and individual transactional systems became important. DRGs drove healthcare in the 1980s and HIT needed to find ways to pull both clinical and financial data in order for reimbursements. The 1990s saw competition and consolidation drive technology to create IDN-like integration. In the 2000s outcomes-based reimbursement became the drive behind developing real-time clinical decision support. For the future, ACOs and value-based purchasing means that CIOs will need to implement data warehouses and analytics application to provide the insights to drive performance improvement necessary for hospital survival.  

The Best Way to Maximize Healthcare Analytics ROI

When it comes to maximizing analytics ROI in a healthcare organization, the more domains, the merrier. Texas Children’s Hospital started their outcomes improvement journey by using an EDW and analytics to improve a single process of care. It quickly realized the potential for more savings and improvement by applying analytics to additional domains, including:

  • Analytics efficiencies
  • Operations/Finance
  • Organization-wide clinical improvement
The competencies required to launch and sustain such an organizational sea change are all part of a single, defining characteristic: the data-driven culture. This allows fulfillment of the analytics strategy, ensures data quality and governance, encourages data and analytics literacy, standardizes data definitions, and opens access to data from multiple sources. This article highlights the specifics of how Texas Children’s has evolved into an outcomes improvement leader, with stories about its successes in multiple domains.

Hadoop in Healthcare: Getting More from Analytics

Healthcare data is positioned for momentous growth as it approaches the parameters of big data. While more data can translate into more informed medical decisions, our ability to leverage this mounting knowledge is only as strong as our data strategy. Hadoop offers the capacity and versatility to meet growing data demands and turn information into actionable insight. Specific use cases where Hadoop adds value data strategy include:

  1. Archiving
  2. Streaming
  3. Machine learning

When Healthcare Data Analysts Fulfill the Data Detective Role

There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:

  1. They are inquisitive and relentless with their questions.
  2. They let the data inform.
  3. They drive to the heart of what matters.
Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.

Understanding Risk Stratification, Comorbidities, and the Future of Healthcare

Risk stratification is essential to effective population health management. To know which patients require what level of care, a platform for separating patients into high-risk, low-risk, and rising-risk is necessary. Several methods for stratifying a population by risk include: Hierarchical Condition Categories (HCCs), Adjusted Clinical Groups (ACG), Elder Risk Assessment (ERA), Chronic Comorbidity Count (CCC), Minnesota Tiering, and Charlson Comorbidity Measure. At Health Catalyst, we use an analytics application called the Risk Model Analyzer to stratify patients into risk categories. This becomes a powerful tool for filtering populations to find higher-risk patients.

Eight Reasons Why Chief Data Officers Will Help Healthcare Organizations Thrive in the Future

The state of healthcare information technology and analytics has evolved to the point where a revised executive structure is advisable in the C-suite. This new structure calls for a Chief Data Officer (CDO) to focus on extracting data from systems and on mining value from that data, rather than getting data into systems, which is the responsibility of the CIO. This article makes the case for the CDO, explains how the need for this emerging role evolved, outlines its responsibilities, advises on how to recruit and budget for this position, and details its domain in eight critical business areas:

  1. Governance and standards
  2. Managing risk
  3. Reducing costs
  4. Driving innovation
  5. Data architecture and technology
  6. Data analytics
  7. Meeting regulatory demand
  8. Creating business value

The Powerhouse Data Analytics and Visualization Tool That Excels

There are many advanced tools that come to mind when considering healthcare data analytics and visualization. Microsoft Excel may not necessarily make the list, but it has distinct advantages, the least of which are that it’s already installed on your system and that you already know how to use it. Healthcare finance folks already know the capabilities of Excel when it comes to quantitative analysis. Excel also deserves a place on the podium when it comes to pulling data from the warehouse and from various source marts. Excel pivot tables are extraordinary for providing ad hoc analysis. And when preceded by dimensional modeling—with the help of Health Catalyst’s data architects—Excel can easily transform large datasets. This article summarizes all of the surprising features that Excel brings to the data analytics and visualization table.

Questions You Should Ask When Selecting a Healthcare Analytics Platform

As vice president of technology for a healthcare IT company, I’m often asked what should be considered when selecting a solution for healthcare analytics. Healthcare organizations have many choices when selecting a healthcare data warehouse and analytics platforms. I advise them to consider the following fundamental criteria: 1) time-to-value (measured in months, not years), 2) experience as a predictor of future success, and 3) extensibility to meet your needs.

4 Ways Healthcare Data Analysts Can Provide Their Full Value

Analysts are most effective when they have the right tools. In healthcare, that means providing data analysts with a means of accessing and testing ALL of the available data and using it to discover more insights. To do this, analysts need guidance more than they need a detailed set of instructions. And, equally as important, they need a data warehouse and access to a testing environment and data discovery tools, so they can truly do the work they were hired to do: analyze.

Factoids Reveal Healthcare Trends in Analytics and Technology

We hand-picked the most interesting, useful, credible factoids from 2015 (including the plethora of facts that came out of the 2nd Healthcare Analytics Summit) to create an easy-to-share presentation. The 32 factoids included in this presentation revealed several interesting healthcare trends:

  • Trend #1: Healthcare analytics continue to improve outcomes and save money. For example, OSF’s predictive readmission model reduced its all-cause readmission rate to less than 10%.
  • Trend #2: New technologies are improving patient engagement. For example, 73 percent of health executives surveyed see positive ROI from personalization technologies, and 76 percent of doctors say patient use of wearable health devices improves engagement.
  • Trend #3: Patients and providers agree on data is useful but have security and interoperability concerns. For example, 83 percent of patients don’t trust EHR safety and security, and 83 percent of physicians are frustrated by EHR interoperability.
Although a majority of healthcare leaders understand the importance of using analytics to improve outcomes and reduce costs, only 15% of hospitals use predictive analytics. We hope to see analytics use increase in 2016, and we’re excited to see how technology will continue to engage patients and lead to better health outcomes.

Improving Patient Safety and Quality through Culture, Clinical Analytics, Evidence-Based Practices, and Adoption

According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents.  Learn key best practices in patient safety and quality including:  patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.

Top 7 Financial Healthcare Trends and Challenges for 2016

Healthcare financial leaders will encounter a myriad of challenges and improvement opportunities in 2016. 2016 will force health system financial leadership to focus and prioritize, with challenges including increased healthcare spending, continued momentum toward value-based care, and the need to reexamine the revenue cycle after years of focusing so intently on ICD-10. But 2016’s financial healthcare trends include more than just challenges; exciting opportunities abound, from using technology to engage patients to a national focus on population health. Engaged healthcare financial leaders—particularly those with the characteristics of effective leaders (resilient, collaborative, and inspirational)—are positioned to stay ahead of the curve in 2016.

Analytics in Community Hospitals: Embracing Data to Thrive in the New Era of Value-Based Care

Value-based care has remade the healthcare landscape for small hospitals. Many are struggling to compete with the larger, better-funded medical centers in the communities they serve. Embracing data and analytics is no longer a luxury for these organizations if they are to succeed and remain competitive. Data analysis can assist senior leaders in identifying opportunities for improvement while balancing long-term goals with short-term pressures. Incorporating data in to the culture and making it a part of everyday decision making will enable smaller hospitals to not only survive, but thrive in the new era of value-based care.

Prospective Analytics: The Next Thing in Healthcare Analytics

Retrospective and predictive analytics are familiar terms for practitioners of clinical outcomes improvement, but the new kid on the block is prospective analytics. This is the next level that uses findings from its predecessors to not only identify the best clinical routes, but also what the results might be of each choice. Prospective analytics gives bedside clinicians an expanded, branching view of operational and clinical options in a type of decision support that can lead to not only improving surgical and medical outcomes, but to making a positive financial contribution, as well. But, as expected with any new process or new way of thinking, prospective analytics requires careful introduction and stewardship to help drive its adoption within the organization.