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Healthcare Informatics Ready to Unleash a New Wave of Advanced Analytics

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Healthcare informatics has come a long way since its founding visionaries saw a way to use technology to extract healthcare data to improve patient care. But a new era has arrived and health systems are now facing the new challenge of maintaining massive amounts of powerful data that’s sitting unused in expensive storage. The next phase of healthcare informatics is for health systems to move from data acquisition to data extraction, so they can use the insights of the data to prioritize which areas will benefit the most by using data to improve quality and reduce costs.

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Healthcare Informatics Ready to Unleash a New Wave of Advanced Analytics

  1. 1. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. © 2014 Health Catalyst www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare Informatics: Ready to Unleash a New Wave of Advanced Analytics? By Steve Barlow, Senior Vice President and Co-Founder
  2. 2. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Informatics Metaphor Look no further than the last scene of Raiders of the Lost Ark to comprehend the current state of healthcare informatics. Specifically when Indiana Jones is being told the government has top men working on discovering the secrets of the Ark of the Covenant, when in fact it is being slowly wheeled into a dark, gigantic warehouse amid what looks to be thousands of other such treasures. They don’t know what they’ve got there!” – Indiana Jones ‘‘
  3. 3. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare Informatics Challenge Much like Indy’s Ark of the Covenant, massive amounts of powerful data sits in storage unused. Health systems have done a great job of acquiring data, especially over the last few years since electronic health records (EHRs) became widespread. But for the most part, the data just sits in a dark warehouse without creating value for those who store it. Fortunately, today’s healthcare executives understand the value of their data and the importance of making it readily available for analysis to drive organizational change, lower costs and improve patient care.
  4. 4. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Triple Aim •Improving the individual experience of care •Improving the health of populations •Reducing the per capita cost of care for populations Healthcare Informatics Challenge By extracting the insights of the stored data, health systems will be able to reduce outcome variations and raise the level of quality and patient satisfaction while driving down the cost of care, the goal of the Institute for Healthcare Improvement’s Triple Aim. SOURCE: IHI TRIPLE AIM Triple Aim • Improving the individual experience of care • Improving the health of populations • Reducing the per capita cost of care for populations
  5. 5. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Birth of Healthcare Informatics Over the past three decades, visionaries saw a need and opportunity for technology and information to support the decision- making process in delivering improved patient care. Pioneers at informatics think tanks, such as the University of Utah, Indiana University, Columbia, and others, have made significant breakthroughs in terms of using data to understand and manage populations.
  6. 6. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare’s Current “Recreational” Data Situation Healthcare’s current data situation is still fairly reactive and historical, however. Brent James at Intermountain Healthcare calls it “recreational data collection,” i.e., collecting data for its own sake — the way some people collect coins or stamps. Or the way Indy collects ancient relics to display in a museum.
  7. 7. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Moving from Data Acquisition to Data Extraction The next frontier is moving from data acquisition to data extraction. This new phase will make it possible for analysts to extract the data out of the EHR, and combine it with data from other systems to provide proactive care to populations of patients. Health systems will need a significant change in strategy to succeed. Budgets must balance between data capture and storage, and data extraction and analysis.
  8. 8. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Using the Data It is important to understand that health systems need more than clinical data for decision-making because this type of data doesn’t provide enough information to answer every quality improvement or patient care question. Health systems also need to analyze financial data, quality data, pharmacy data, and other factors that can affect the quality and cost of care.
  9. 9. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Using the Data By connecting all of the various types of healthcare data, it will then be possible to answer questions such as these: Which hip prosthetic delivers the best outcomes, both from a cost and a quality of life standpoint? How can we find and eliminate variations in care delivery in order to improve outcomes and lower costs? Why did this population of patients, treated at this hospital, have better quality and financial outcomes than those patients at other hospitals?
  10. 10. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Using the Data Understanding the entire picture of patient care is best accomplished by aggregating data from the individual source systems into a Late-Binding™ electronic data warehouse (EDW). Informatics analysts can use powerful analytics applications to make new knowledge discoveries to support clinical decisions and adherence to standards across patient populations.
  11. 11. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Using the Data A good example is the accepted standard of not performing elective inductions on pregnant mothers before 39 weeks. Most healthcare organizations understand the value of this standard in reducing risks to the mother, the newborn, and the provider, and have rules in place to support it. Yet when we analyze the EHR data for our clients, we often discover that 15 percent of the time they are not adhering to the standard.
  12. 12. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Determining Which Data is Useful One of the best ways to decide which data is useful is by looking at which data is being used and which is not. If no one is looking at the report or using the data why bother keeping it? Because that’s the way it’s always been done is not a good reason. Instead, health systems should focus their data capture on the information that assists clinical decisions at the point of care.
  13. 13. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Placing Effort Where the Greatest Value Lays The best way to launch a quality initiative is to start where the changes will do the most good. In selecting a clinical process for quality improvement, choose the process with high volumes that will result in greatest ROI. For example, efforts and resources to focus on hand procedures is nearly the same as focusing on patients with congestive heart failure (CHF). However, the volumes will be much higher with CHF patients, thus a higher ROI.
  14. 14. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Looking Ahead The Rise of the Healthcare Consumer The day is coming when consumers shop for providers and procedures, such as hip replacements or even open heart surgery, the same way they shop for cars — armed with a printout and consumer reviews. To survive and thrive in this new world of healthcare informatics, providers will need to move from collecting data to analyzing it for critical insights, and from there to knowledge and finally wisdom — the classic maturation cycle of data.
  15. 15. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. More about this topic Unlocking the Power of Data at Texas Children’s Hospital An article from Healthcare Informatics Healthcare Data Analytics Can Prevent Early-Term Deliveries An article from Healthcare Informatics Building a High-quality Cancer Care Delivery System A health system case study on using analytics to address rising cancer care costs
  16. 16. © 2013 Health Catalyst www.healthcatalyst.com Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Steve Barlow is a co-founder of Health Catalyst and former CEO of the company. He oversees all development activities for Health Catalyst's suite of products and services. Mr. Barlow is a founding member and former chair of the Healthcare Data Warehousing Association. He began his career in healthcare over 18 years ago at Intermountain Healthcare, and acted as a member of the team that led Intermountain's nationally recognized improvements in quality of care delivery and reductions in cost. Mr. Barlow holds a BS from the University of Utah in health education and promotion. More by this author: What Is the Best Healthcare Data Warehouse Model? Comparing Enterprise Data Models, Independent Data Marts, and Late-Binding™ Solutions 6 Reasons Why Healthcare Data Warehouses Fail The Best Data Architecture: Know When to Bind Your Healthcare Data

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