Does new health IT adoption in hospitals actually impact patient outcomes?

In my last post we talked about how to employ a successful health IT implementation at a hospital. After hospital staff accept and get accustomed to the new processes that are brought by the health IT solutions, a natural question that follows would be how effective these health IT solutions are. In other words, how does health IT adoption in hospitals impact patient outcome? Researchers McCullough, Parente, and Town published an article in 2016 on the RAND Journal of Economics examining exactly this question.

To study this question, they compiled IT adoption data from 4000 hospitals as well as diagnosis and outcomes of their Medicare, fee-for-service (FFS) patients during 2002-2007. The IT solutions they looked at are the Electronic Medical Record (EMR) and Computerized Provider Order Entry (CPOE). necEMRs systematically collect patients’ health information replacing traditional medical charts. CPOE allows providers to electronically enter medical orders for patient services and medications, thus reducing opportunities for miscommunication between disparate care providers. They studied the effect of EMR and CPOE on 3 types of patient outcomes: 60-day mortality rates, length of stay and 30-day hospital readmission.

They hypothesize that Health IT solutions positively affect patient outcomes through two mechanisms: 1) clinical decision support, and 2) information management and care coordination. Clinical decision support can include things like providing rule-based treatment guidelines or preventing drug prescribing errors. Health IT can support information management and care coordination because many conditions require extensive monitoring and testing, and generation of large quantities of clinical information. Health IT solutions can be used to capture and organize these data, therefore expediting and improving treatment decisions. When patients need multiple specialists to work together to come up with a treatment plan, IT solutions can help physicians access their colleague’s treatment decisions, therefore reducing communication and coordination barriers.

In studying patient outcome, they focus on 4 conditions: acute myocardial infarction (AMI), congestive heart failure (CHF), coronary atherosclerosis (CA) and pneumonia. These conditions were selected because they are common, mortality is a common outcome and health IT can plausibly reduce medical errors and improve the quality of care.

At first, their research findings suggests that health IT adoption does not affect outcomes for the median patient. As they dug deeper, they found that the actual impact of health IT adoption on patient outcomes is more subtle. They decomposed patient conditions at different severity levels and found that while health IT has no measurable benefits for relatively healthy patients, it significantly decreases mortality for relatively high-risk PN, CHF and CA patients. In other words, the effect of healthcare IT is small for low-severity patients but the benefits from IT adoption increase with severity. Their results also show little support for the hypothesis that health IT improves quality through rules-based decision support. Rather, health IT improves quality by facilitating coordination and communication across providers and by helping providers manage clinical information.

Their findings also showed that health IT adoption affects patient outcomes differently and the effect on conditions varies, too. They found no effect on AMI and no relationship between health IT and either readmissions or length of stay. Rather, they found an average mortality reduction of approximately 200 deaths per 100,000 admissions from IT adoption. The impact is largest for PN where IT adoption is estimated to prevent 500 deaths per 100,000 admissions while IT adoption reduces approximately 10 deaths per 100,000 admissions for both CA and CHF.

These days more and more hospitals are adopting health IT solutions like the EMR. This research shows that they are most effective for patients with severe diagnoses and they can reduce mortality rate by improving information management and coordination.

Citation:

Jeffrey S. McCullough, Stephen T. Parente, and Robert J. Town. “Health Information Technology and Patient Outcomes: The Role of Information and Labor Coordination.” The RAND Journal of Economics. Vol. 27, no. 1 (2016): 207-236.

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about the author

Yixin Qiu

Yixin Qiu is a Project Manager with Qlarant and is involved in anything related to our Predictive Modeling Solution, PLATO™. Yixin works with developers, modelers, SMEs, as well as outreach and support to deliver a high quality software platform with end users in mind. See all posts from Yixin Qiu.