Elliot G. Arsoniadis

Automated Detection of Postoperative Surgical Site Infections Using Supervised Methods with Electronic Health Record Data

The National Surgical Quality Improvement Project (NSQIP) is widely recognized as “the best in the nation” surgical quality improvement resource in the United States. In particular, it rigorously defines postoperative morbidity outcomes, including surgical adverse events occurring within 30 days of surgery. Due to its manual yet expensive construction process, the NSQIP registry is of exceptionally high quality, but its high cost remains a significant bottleneck to NSQIP’s wider dissemination. In this work, we propose an automated surgical adverse events detection tool, aimed at accelerating the process of extracting postoperative outcomes from medical charts. As a prototype system, we combined local EHR data with the NSQIP gold standard outcomes and developed machine learned models to retrospectively detect Surgical Site Infections (SSI), a particular family of adverse events that NSQIP extracts. The built models have high specificity (from 0.788 to 0.988) as well as very high negative predictive values (>0.98), reliably eliminating the vast majority of patients without SSI, thereby significantly reducing the NSQIP extractors’ burden.

Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record

Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for analyzing EHR data is limited and specific efficacy for postoperative complication detection is unclear. Several data imputation methods were used to develop data models for automated detection of three types (i.e., superficial, deep, and organ space) of surgical site infection (SSI) and overall SSI using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) Registry 30-day SSI occurrence data as a reference standard. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values. Missing data imputation appears to be an effective means for improving postoperative SSI detection using EHR clinical data.

African Americans and Short-Term Outcomes after Surgery for Crohn's Disease: An ACS-NSQIP Analysis.

Background Previous reports on racial disparities in the treatment of Crohns disease [CD] in African American [AA] patients have shown differences in both medical and surgical treatments in this population. No study thus far has examined the effect of AA race on outcomes after surgery for CD. Methods Utilizing the National Surgical Quality Improvement Program [NSQIP] Participant User File [PUF] for the years 2005-2013, we examined the effect of AA race on postoperative complications in patients with CD undergoing intestinal surgery. Results AA patients had a significantly higher rate of complications overall compared to non-AA patients [23.5% vs 18.9%, p = 0.002]. Postoperative sepsis [10.9% vs 6.6%, p < 0.001] and surgical site infection [17.6% vs 14.8%, p = 0.037] were most significant. After adjustment for age, sex, preoperative disease severity and lifestyle factors [smoking], race remained a statistically significant factor in postoperative complication rate. Only after additional adjustment was made for comorbidities and American Society of Anesthesiologists class did race lose significance within our model. Conclusion African Americans experience a greater amount of postoperative complications following surgery for Crohns disease. Preoperative disease management, addressing smoking status and control of comorbid disease are important factors in addressing the racial disparities in the surgical treatment of Crohns disease.

Characterizing Patient-Generated Clinical Data and Associated Implications for Electronic Health Records.

Patient-facing technologies are increasingly utilized for direct patient data entry for potential incorporation into the electronic health record. We analyzed patient-entered data during implementation of a patient-facing data entry technology using an online patient portal and clinic-based tablet computers at a University-based tertiary medical center clinic, including entries for past medical history, past surgical history, and social history. Entries were assessed for granularity, clinical accuracy, and the addition of novel information into the record. We found that over half of patient-generated diagnoses were duplicates of lesser or equal granularity compared to previous provider-entered diagnoses. Approximately one fifth of patient-generated diagnoses were found to meet the criteria for new, meaningful additions to the medical record. Our findings demonstrate that while patient-generated data provides important additional information, it may also present challenges including generating inaccurate or less granular information.

Characterizing surgical site infection signals in clinical notes

Surgical site infections (SSIs) are the most common and costly of hospital acquired infections. An important step in reducing SSIs is accurate SSI detection, which enables measurement and quality improvement, but currently remains expensive through manual chart review. Building off of previous work for automated and semi-automated SSI detection using expert-derived “strong features” from clinical notes, we hypothesized that additional SSI phrases may be contained in clinical notes. We systematically characterized phrases and expressions associated with SSIs. While 83% of expert-derived original terms overlapped with new terms and modifiers, an additional 362 modifiers associated with both positive and negative SSI signals were identified and 62 new base observations and actions were identified. Clinical note queries with the most common base terms revealed another 49 modifiers. Clinical notes contain a wide variety of expressions describing infections occurring among surgical specialties which may provide value in improving the performance of SSI detection algorithms.

Supplementing Resident Research Funding Through a Partnership With Local Industry

OBJECTIVE To develop a model for the supplementation of resident research funding through a resident-hosted clinical immersion with local industry. DESIGN Designated research residents hosted multiple groups of engineers and business professionals from local industry in general surgery-focused clinical immersion weeks. The participants in these week-long programs are educated about general surgery and brought to the operating room to observe a variety of surgeries. SETTING This study was performed at the University of Minnesota, in Minneapolis, Minnesota, at a tertiary medical center. PARTICIPANTS Ten designated research residents hosted general surgery immersion programs. Fifty-seven engineers and business professionals from 5 different local biomedical firms have participated in this program. RESULTS General surgery research residents (in collaboration with the University of Minnesotas Institute for Engineering in Medicine) have hosted 9 clinical immersion programs since starting the collaborative in 2015. Immersion participant response to the experiences was very positive. Two full-time resident research positions can be funded annually through participation in this program. CONCLUSIONS With decreasing funding available for surgical research, particularly resident research, innovative ways to fund resident research are needed. The general surgery clinical immersion program at the University of Minnesota has proven its value as a supplement for resident research funding and may be a sustainable model for the future.

Assessing Provider-Generated Free-Text Quality in EHR-Integrated Handoff Notes.

Handoff notes are increasingly integrated within electronic health record (EHR) systems and often contain data automatically generated from the EHR and free-text narratives. We examined the quality of data entered by providers in the free-text portion of our institutional EHR handoff tool. Overall, 65% of handoff notes contained at least one error (average 1.7 errors per note). Most errors were omissions in information around patient plan/management or assessment/diagnosis rather than entry of false data. Factors associated with increased error rate were increasing hospital day number; weekend note; medical (vs. surgical) service team; and authorship by a medical student, first or fourth year resident physician, or attending physician. Our findings suggest that errors are common in handoff notes, and while these errors are not completely false data, they may provide individuals caring for patients an inaccurate understanding of patient status.

Usability testing of two ambulatory EHR navigators

BACKGROUND Despite widespread electronic health record (EHR) adoption, poor EHR system usability continues to be a significant barrier to effective system use for end users. One key to addressing usability problems is to employ user testing and user-centered design. OBJECTIVES To understand if redesigning an EHR-based navigation tool with clinician input improved user performance and satisfaction. METHODS A usability evaluation was conducted to compare two versions of a redesigned ambulatory navigator. Participants completed tasks for five patient cases using the navigators, while employing a think-aloud protocol. The tasks were based on Meaningful Use (MU) requirements. RESULTS The version of navigator did not affect perceived workload, and time to complete tasks was longer in the redesigned navigator. A relatively small portion of navigator content was used to complete the MU-related tasks, though navigation patterns were highly variable across participants for both navigators. Preferences for EHR navigation structures appeared to be individualized. CONCLUSIONS This study demonstrates the importance of EHR usability assessments to evaluate group and individual performance of different interfaces and preferences for each design.

Analyzing Operative Note Structure in Development of a Section Header Resource.

Operative notes contain essential details of surgical procedures and are an important form of clinical documentation. Sections within operative notes segment provide high level note structure. We evaluated the HL7 Implementation Guide for Clinical Document Architecture Release 2.0 Operative Note Draft Standard for Trial Use (HL7-ON DSTU) Release 1 as well as Logical Observation Identifiers Names and Codes (LOINC®) section names on 384 unique section headers from 362,311 operative notes. Overall, HL7-ON DSTU alone and HL7-ON DSTU with LOINC® section headers covered 66% and 79% of sections headers (93% and 98% of header instances), respectively. Section headers contained large numbers of synonyms, formatting variation, and variation of word forms, as well as smaller numbers of compound sections and issues with mismatches in header granularity. Robust operative note section mapping is important for clinical note interoperability and effective use of operative notes by natural language processing systems. The resulting operative note section resource is made publicly available.