# Secondary use of EMRs for surgical complication surveillance

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2020 · $643,686

## Abstract

Project Summary
Post-surgical complications (PSCs) have been an increasing concern for hospitals, particularly in light of
payment reform focusing on longer episodes and Medicare penalties for 30-day readmissions and adverse
outcomes including deep or organ space surgical site infections (DOS-SSIs). Readmission due to post-
discharge complications in particular has become a target for quality improvement since many of these events
are considered preventable. The wide adoption of electronic health records (EHRs) has led to a number of
clinical risk models for PSCs. These modeling efforts have primarily been targeted at the surgical specialty
areas within which a large number of events occur (such as colorectal surgery) as well as applying
sophisticated statistical modeling / machine learning to allow for missing data, interactions, and nonlinearities.
However, there is still considerable room for improvement both in terms of accuracy and generalizability. In our
current funding period, we have demonstrated the predictive value of clinical notes for PSCs. However, one
glaring limitation of current models is that they are trained on high volume surgical specialties at large tertiary
care institutions with high quality clinical data and use of advanced informatics approaches. The impetus of this
proposal is essentially two-fold: (i) Accurate models can be created for lower volume institutions and
specialties via transfer learning and leveraging more data via unconfirmed outcomes (i.e., those that mimic
gold standard outcomes, but are less reliable) with proper accounting of reliability. (ii) Decision making can be
significantly improved by leveraging time varying, real-time data such as labs, vitals, and clinical notes to
provide the current risk of PSCs for patients using all information as it becomes available. We aim to i) develop
and apply longitudinal risk models for PSCs to explicitly account for the time varying nature of some of the
information (e.g., labs, vitals, clinical notes) as it becomes available in real-time so that it can be integrated into
the clinician’s decision making; ii) develop and apply transfer learning to PSC risk models; iii) develop modeling
approaches that allow for the use of more widely available unconfirmed outcomes, while explicitly accounting
for the additional uncertainty and bias due to the use of such unconfirmed outcomes when compared to a less
available gold standard; and iv) develop a widely applicable framework for model evaluation and monitoring.
Models will often not perform in practice as they do in research for a variety of reasons. This framework will
allow us to identify these issues and more efficiently translate and apply these complex predictive models into
practice so that the research can have an immediate clinical impact. Successful development would open the
door for next generation patient monitoring, alerts, and interventions for all surgical specialties and all
institutions. We will make the ...

## Key facts

- **NIH application ID:** 10001498
- **Project number:** 5R01EB019403-06
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** HONGFANG LIU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $643,686
- **Award type:** 5
- **Project period:** 2015-05-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10001498

## Citation

> US National Institutes of Health, RePORTER application 10001498, Secondary use of EMRs for surgical complication surveillance (5R01EB019403-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10001498. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*