The Power and Promise of Unstructured Patient Data
Unstructured search capabilities, superior natural language processing, and healthcare ontology capabilities will help distinguish the leading products information and data-driven decision making.
Source: JEGI, Gartner, McKinsey, ADA, AHA, HealthPartners Research Foundation, Healthline analysis McKinsey estimates the U.S. can save $300B-$450B per year from investments in Big Data analytics 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 1 2 3 4 $ Trillions What curve would look like with savings from successful use of Big Data U.S. Spending on Healthcare 2012 2013 2014 2015
Analytics Tech investments shifting from collecting data to understanding it to making it actionable at the point of care Data Latency Reporting Analytics What happened? What will happen? Why did it happen? What is happening? What should we do? What can we offer? Data Information Knowledge Data Freshness
Mind Source: SearchHealthIT.com's business intelligence survey 0 10 20 30 40 50 60 70 80 other none administrative business intelligence predictive analysis data mining clinical data analysis Which advanced analytics tools does your organization plan to you use in the next 2 years? Results based on 243 responses from CIOs and senior IT executives at medical centers, health systems and physician practices across U.S.
over the next decade will be unstructured (IDC, Kaiser Family Foundation) • Healthcare is moving to a value based model • Providers need to make investments in data-driven technologies to manage the health of their patient populations more effectively • A major factor mitigating the power of these analytics solutions is access to information-rich unstructured data (e.g., physician notes, family histories, etc.) • Leveraging data—structured and unstructured—from disparate sources is key Leveraging Unstructured Data and Data from Disparate Sources Is Critical
ontology capabilities will help distinguish the leading products in the category (information and data-driven decision making). Robust Health Informatics is the Key to Unlocking the Unusable Data “ “ Source: JEGI HCIT Issues, Trends and M&A Outlook 2014
IS IN THE HIGHEST RISK CATEGORY BASED ON A VARIETY OF FACTORS: 1. Medical / Health Factors 2. Psycho-Social Factors 3. Socio-Economic Factors Understanding who is a highest risk for readmission makes the targeting of scare resources in terms of interventions and support possible at scale.