Discussion

Residence or recent stay in an LTCF is one of the most important risk factors for MDRO carriage and infection, making reliable identification of LTCF-exposed inpatients a critical priority for infection control day-to-day practice and research (eg, to guide targeted screening decisions, contact isolation initiation, or conduct outbreak investigations). However, because most hospital EHRs do not include a dedicated field for documenting LTCF exposure, absent manual, and time-consuming review of patient charts, identifying LTCF-exposed inpatients is challenging. Two universally mandated EHR billing fields, hospitalization admission source and discharge location, offer a potential alternative strategy for identifying LTCF-exposed inpatients. Analyzing more than 1,000 randomly sampled admissions between 2016 and 2021 across 12 UMMS hospitals, we found that when used individually, each field misses many LTCF-exposed patients, but sensitivity increases to 76% compared to the clinical notes when evaluating admission source and discharge location together.

Our study was stratified by LTCF exposure timing. For identifying current LTCF exposure, ie, exposure that immediately preceded hospitalization, the H&P note significantly outperformed the admission source field. Specifically, the H&P clinical note identified most patients with current LTCF exposure (92%), double the identification rate (46%) of admission source. These results are consistent with findings from a smaller 2012 study by Prabaker et al ^{Reference Prabaker, Hayden, Weinstein and Lin17} that also evaluated the H&P note and admission source field for identifying current LTCF exposure. That study of 523 inpatients at Rush University Medical Center similarly found that the H&P note outperformed the admission source field, with respective sensitivities of 71% and 50% compared to patient interview. ^{Reference Prabaker, Hayden, Weinstein and Lin17}

In manual review, we found that most currently LTCF-exposed patients missed by the admission source field had admission sources of “home” and “self-referral,” and some of these patients first presented to the Emergency Department (ED). A further 26% of missed patients were direct transfers from other acute care hospitals. These alternative admission sources help explain the admission source field’s poor sensitivity for identifying LTCF exposure, because preadmission visits to the ED or other hospitals “break the chain” of direct admission from an LTCF. Prabaker et al identified similar patterns of missingness in their earlier study, including that many LTCF patients were transported to the hospital by family members rather than as direct LTCF transfers via ambulance. Interestingly, they also found that one-third of LTCF patients were non-verbal and thus could not have answered an LTCF intake questionnaire, even had the hospital implemented one. ^{Reference Prabaker, Hayden, Weinstein and Lin17} Taken together, both our and Prabaker et al’s findings suggest that absent a dedicated LTCF-exposure ascertainment mechanism (eg, a statewide database ^{Reference Lin, Ray, Rezny, Runningdeer, Weinstein and Trick20} or family interview), identifying current LTCF-exposed inpatients requires clinical documentation. Given the resource-intensiveness of manual chart review, the development of automated NLP classifiers for extracting LTCF information from free-text notes, if appropriately validated, could thus substantially assist hospital epidemiology and infection control efforts.

MDRO carriage generally persists for months or years, ^{Reference Lima, Sousa and Filgueira21,Reference Weintrob, Roediger and Barber22} and any recent LTCF exposure may increase MDRO risk. Therefore, we also evaluated the admission and discharge locations for admissions occurring within 90 days preceding the index admission, which to our knowledge is the first validation of these data fields for identifying recent LTCF exposure. As with current LTCF exposure, we found that the admission source for the index admission and any recent admissions had poor sensitivity (32%) for identifying patients with any recent LTCF exposure. These findings are broadly consistent with published literature for non-LTCF healthcare facilities, which documents that many inpatients with recent, previous hospitalizations are not admitted as direct hospital transfers. ^{Reference Lee, McGlone and Song23} Evaluating the discharge location for previous admission(s) identified a greater percentage—57%—of LTCF-exposed patients. However, an approach that evaluated the admission source and discharge location fields together was the highest-performing strategy, achieving 76% sensitivity compared to the H&P note for detecting any recent LTCF exposure (95% CI: 67%–83%). These results suggest that patients admitted from and discharged to LTCFs represent largely distinct populations, a finding that is clinically intuitive insofar as many patients discharged to rehabilitation and SNFs do not enter from LTCFs and are not permanent LTCF residents.

Although the admission source and discharge location fields together identified 76% of LTCF-exposed patients compared to the H&P note, researchers and practitioners interested in using these variables to ascertain LTCF exposure should understand 2 important caveats. First, these data fields still missed most currently exposed LTCF patients, particularly those who had not been recently hospitalized. For projects aiming to identify current LTCF exposure, clinical note review would likely remain necessary. Second, identifying recent LTCF exposure from structured EHR data relies on “seeing” previous admissions and, in turn, the admission and discharge locations from these admissions. This visibility will vary by healthcare network size and geographic capture area. UMMS is a large network, increasing the likelihood that previous admissions were in-network and documented in our EHR. Even across our UMMS population, however, 22% of patients hospitalized in the previous 90 days had been hospitalized at a different UMMS hospital than for their index admission (data not shown). As these data underscore, patients do not always return to the same hospital, and had these previous admissions between out-of-network, our EHR would not have included them. When using the EHR admission and discharge location fields to identify LTCF-exposed inpatients in either smaller hospital networks or networks with less geographic capture, we would expect lower sensitivity compared to the H&P note than the 76% demonstrated in this study.

As a multi-center study spanning multiple years, this validation benefited from a large and diverse sample, increasing statistical power and generalizability. However, the retrospective study design that enabled these strengths also meant that our study is subject to several limitations, principally that we were unable to include a gold-standard comparator for assessing LTCF exposure (eg, family interview). Our study mirrors real-world infection control practice and clinical research, however, for which chart review generally constitutes the de facto reference standard for identifying LTCF-exposed patients. ^{Reference Marchaim, Chopra and Bogan8,Reference Cunha, Kassakian and Chan24} Moreover, and importantly, the lack of a gold-standard comparator does not affect sensitivity comparisons between data sources (eg, between the H&P note and the admission source field). Nevertheless, because missingness of LTCF exposure in patient data may be non-random, calculating robust estimates of true LTCF exposure among inpatients would require prospective studies (eg, pilot deployments of intake questionnaires under quality improvement initiatives). Second, a review of clinical notes is labor- and time-intensive, and we prioritized reviewing index admission notes to maximize study sample size. Given resource constraints, we were unable to review H&P notes for hospitalizations that preceded the index admission or other note types. In practical effect, this decision decreased the sensitivity of the H&P notes for identifying any recent LTCF exposure, compared to the admission and discharge location fields from previous admissions. In future research, we plan to develop NLP classifiers for identifying LTCF exposure, which when executed across admissions and note types (and potentially patient addresses, cross-referenced to local LTCF addresses) should increase clinical note sensitivity without increasing human labor time. Third, LTCF definitions vary across research studies and data sources, for example, whether ALFs are included ²⁵ or excluded. ^{Reference Prabaker, Lin and McNally10,Reference Prabaker, Hayden, Weinstein and Lin17} Because human syntax is imprecise (eg, physicians may use the terms “assisted living” and “nursing home” interchangeably), we employed a broad LTCF definition that included ALFs and rehabilitation facilities. While this decision increased study internal validity for comparing the clinical notes to the structured EHR data fields, our validation results do not extend to narrower definitions of LTCF exposure. Finally, we selected a 90-day lookback period due to its clinical and infection control relevance, with research demonstrating that LTCF patients have an average MDRO colonization duration of approximately 3 months. ^{Reference Lin, Huang, Pan, Chen and Li26} However, future validation of these EHR data fields for longer lookback periods would be an important extension of the current work.

Overall, in this multi-center validation study, we found that compared to the H&P note, the EHR admission source and discharge location fields identified three-quarters of inpatients with recent LTCF exposure but missed most patients with current LTCF exposure (eg, permanent LTCF residents). From a practical perspective, our findings suggest 3 take-home points for using EHR data to identify LTCF-exposed inpatients: (1) do not rely solely on the hospital “admission source” field, which misses most LTCF-exposed patients, (2) review the clinical notes to identify patients with current LTCF exposure, and (3) examine discharge locations from recent admissions to identify patients who were recently LTCF-exposed, even if reviewing the clinical notes. As technological advances ease the barriers to extracting information from free-text data, the ideal strategy for identifying LTCF-exposed inpatients will likely involve triangulating data from the unstructured notes and the structured admission source and discharge location fields. Given the importance of accurately ascertaining LTCF exposure, however, we also encourage hospitals to consider implementing intake questionnaires, with structured response fields, for documenting LTCF exposure in the EHR.