Health Affairs explores the promise of big data in improving health care effectiveness and efficiency in its July issue. Many articles examine the potential of approaches such as predictive analytics and address the unavoidable privacy implications of collecting, storing, and interpreting massive amounts of health information.
Big data can yield big savings, if the data are used in the right ways.
David Bates of the Brigham and Women’s Hospital and coauthors analyze six use cases with strong opportunities for cost savings: high-cost patients; readmissions; triage; decompensation (when a patient’s condition worsens); adverse events; and treatment optimization when a disease affects multiple organ systems.
The authors suspect that cost savings will vary widely, though the current costs associated with all six scenarios will be significant. They suggest that using analytics for multiple conditions is likely to yield even stronger cost savings.
Bates and coauthors highlight key areas for policy makers to emphasize in this environment, including federal investment in research into analytics and big data; a clearer regulatory stance at the Food and Drug Administration regarding the use of these tools; a strengthening of incentives for providers to control costs; and more defined privacy parameters regarding the use of big data at the federal level.
Don’t get sick over the weekend, or go to a hospital with a lot of sick people already in beds.
Flemming Madsen of the Allergy and Lung Clinic in Helsingoer, Denmark, and coauthors examine the effects of hospital bed shortages on patient outcomes using 2.65 million admissions to Danish hospitals from 1995–2012. They report that while high rates of bed occupancy (above 85 percent) can be a sign of more efficient care, they were also associated with a 9 percent increase in rates of both in-hospital mortality and thirty-day mortality.
In addition, the researchers found that patients who were admitted outside of normal working hours had higher inpatient and thirty-day mortality rates, especially among the elderly. They also suggest that chronic bed shortages are likely a product of a self-regulating mechanism and deliberate planning to keep occupancy rates high.
Madsen and colleagues recommend approaching high-occupancy rates as a public health issue as opposed to a purely economic one. They point out that the rapidly aging U.S. population will further strain hospitals both with and without emergency departments.
Turns out fears of widespread hospital “upcoding” with EHRs were unfounded.
Julia Adler-Milstein of the University of Michigan Schools of Information and Public Health and Ashish Jha of the Harvard School of Public Health analyzed national longitudinal data to determine whether new adoption of electronic health records (EHRs) would result in hospitals selecting billing codes for more intensive care or sicker patients to increase their Medicare payments, or “upcoding.”
Despite widespread stories and concerns among policy makers about this potential behavior, the authors found that both EHR adopters and non-EHR adopters increased their billing to Medicare at comparable rates over the period examined. They also found no appreciable difference between adopters and non-adopters in coded patient acuity.
Adler-Milstein and Jha conclude that resources for policy interventions to reduce this type of presumed fraud are likely better applied to ensure the use of EHRs for better quality of care and reduced health care spending.
More Federally Qualified Health Centers are adopting EHRs—but could use help making them “meaningful.”
Emily Jones of the Office of the Assistant Secretary for Planning and Evaluation in the Department of Health and Human Services and Michael Furukawa of the Agency for Healthcare Research and Quality found a 25 percent increase in the number of federally qualified health centers (FQHCs) with an EHR system between 2010 and 2012 (90 percent versus 64.8 percent).
They also found that 49.5 percent reported meeting the basic EHR system criteria, up from 29.7 percent over the same period. The authors uncovered new disparities in EHR adoption, with smaller centers, those with higher percentages of lower-income patients, and centers in the Midwest lagging behind their counterparts.
Despite strong growth overall, Jones and Furukawa note, only one-third of the centers were ready for Stage 1 “meaningful use” standards as determined by the Centers for Medicare and Medicaid Services. In addition, Stage 1 readiness was found to be associated with quality recognition. The authors recommend targeting resources to centers with slower uptake and connecting providers with technical assistance to better prepare FHQCs for compliance with meaningful use requirements.
Comparative Effectiveness and Predictive Analysis
A series of papers focus on comparative effectiveness and predictive analysis:
- Implementing Electronic Health Care Predictive Analytics: Considerations And Challenges by Ruben Amarasingham of the Parkland Center for Clinical Innovation and coauthors
- The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care by I. Glenn Cohen of Harvard Law School and colleagues
- Insights From Advanced Analytics At The Veterans Health Administration by Stephan Fihn of the Veterans Health Administration and colleagues
- Patient-Powered Research Networks Aim To Improve Patient Care And Health Research by Rachael Fleurence of the Patient-Centered Outcomes Research Institute and colleagues
- Assessing The Value Of Patient-Generated Data To Comparative Effectiveness Research by Lynn Howie of Duke University and colleagues
- Optum Labs: Building A Novel Node In The Learning Health Care System by Paul Wallace of Optum Labs and colleagues
The July issue of Health Affairs was supported by the Gordon and Betty Moore Foundation, United Health Foundation, the Patient-Centered Outcomes Research Institute, Merck, Pfizer, IBM, the John A. Hartford Foundation, the California Health Care Foundation, and the Robert Wood Johnson Foundation.