NYU Langone Health implements randomized quality tests

The goal is to run dozens of such quality improvement projects each year and to ultimately make randomization standard practice for the continual improvement of existing and new programs.
By Nathan Eddy
09:10 PM

Part of the team responsible for randomized quality improvement projects carried out at New York University’s Langone Health medical center.

A series of randomized quality improvement projects carried out at New York University’s Langone Health medical center has helped the organization better assess the effectiveness of patient-focused programs.

The efforts to evaluate the effectiveness of routine processes of hospital staff ranged from inpatient units and outpatient offices to the emergency department, as well as adjustments to post-discharge services like phone calls or text messages to patients.

The findings of the projects, led by Leora Horwitz, an associate professor of population health and medicine and director of the Center for Healthcare Innovation and Delivery Science, were published in the September 19, 2019, issue of the New England Journal of Medicine.

Running dozens of QI projects

The goal is to run dozens of such quality improvement projects each year and to ultimately make randomization standard practice for the continual improvement of existing programs, in addition to the implementation of new programs.

Areas of particular focus in the coming year include electronic health record-based alerts, which can improve quality and safety but may also increase burnout and alert fatigue, and care coordination activities, which are resource-intensive, but the researchers feel have high potential for benefit if implemented effectively.

Dr. Horwitz said overall, healthcare digitalization can help improve quality of care in a variety of ways.

"We can use data to add safety measures like alerts for abnormal results, dangerous interactions or missed interventions; we can now do aggregate analyses of large populations to identify patterns that affect quality and much more," she explained.

Abnormal results can be lost

However, she noted that when data are not well organized or aggregated, then "we are at risk of losing the forest for the trees," and abnormal results can be lost in the morass.

"Inaccurate information, once entered, can be hard to correct. Free text data is typically not included in safety algorithms or clinical decision support," she explained.
"The plethora of 'FYI' messages and patient-generated messages to clinicians increases the non-clinical workload and cognitive burden."

In addition, Dr. Horwitz pointed out sub-optimally designed systems can increase the time it takes to document essential information and may increase burnout rates.

The learning health system program is housed in the Center for Healthcare Innovation and Delivery Science at NYU Langone Health, which provides an experienced healthcare delivery scientist, a project manager, a project assistant, a data analyst and a statistician with a total cost to the institution of less than $350,000 per year.

Better infrastructure is required

The report noted that in order to run projects with less assistance, better infrastructure is required; for example, standardized data extraction and analytic code, particularly for projects based on electronic health records, as well as reporting templates that would automatically generate final tables and figures.

"Healthcare institutions are facing increasing ethical, regulatory and financial imperatives to improve care," the report concluded. "Rapid-cycle, randomized quality improvement projects are a potentially extremely effective, low-cost, but underused tool in creating a learning health system that achieves the Triple Aim of providing better health and healthcare at lower cost."

Nathan Eddy is a healthcare and technology freelancer based in Berlin.
Email the writer: nathaneddy@gmail.com
Twitter: @dropdeaded209

Healthcare IT News is a publication of HIMSS Media.
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