Targeted use of postoperative discharge phone calls to reduce early readmission rates.

OBJECTIVES: Postoperative readmissions are common and costly. Office-initiated phone calls to patients shortly after discharge may identify concerns and allow for early intervention to prevent readmission. We sought to evaluate our 30-day readmission rate after the implementation of a standardized postoperative discharge phone call (PODPC) intervention, compared with a historical aggregated cohort. METHODS: From July 2020 to 21, postoperative patients were prospectively identified at 48 hour after discharge. Medical assistants performed PODPCs, administering a survey designed to identify medical/surgical issues that could signify a complication and warrant escalation to a nurse practitioner (NP) for further management. Demographics, comorbidities, and procedure type were obtained retrospectively. Descriptive statistics were used to evaluate PODPC responses, frequency of escalation, readmission, and reasons. The electronic medical record identified a historical aggregated cohort (July 2018 to 2019) and the 30-day readmission rate. A χ2 analysis was used to compare readmission rates between the preintervention historical and PODPC intervention groups. Predictors of 30-day readmission were modeled with multivariable logistic regression. RESULTS: Of 411 PODPCs conducted, 106 patients (26%) reported not feeling well; having concerns. Eighty-four PODPCs (20%) triggered escalation to a NP; of these, 60 patients (71%) were counseled over the phone by an NP, 16 (19%) were brought into clinic, 6 (7%) were sent to the emergency department, and 2 (2%) did not answer the NP call. Of 411 patients, 17% (n = 68) were readmitted within 30 days. Comparatively, the historical aggregated cohort readmission rate was significantly higher at 28% (n = 346; P < .001). On multivariable analysis, chronic obstructive pulmonary disease (odds ratio [OR], 1.92; 95% confidence interval [CI], 1.01-3.65; P = .046), and feeling run down; having difficulty with movement; needing assistance for most activities (OR, 3.94; 95% CI, 2.09-7.43; P < .0001) were predictive of 30-day readmission when controlling for procedure type. CONCLUSIONS: Although readmissions remained common (>15%), being in the intervention cohort was associated with a significantly lower readmission rate compared with the historical aggregated cohort. One-fifth of PODPCs identified a concern; however, >90% of these could be managed by an NP by phone or in clinic. This PODPC intervention holds promise as a viable mechanism for decreasing readmissions.

Successful implementation of a nurse-navigator-run program using natural language processing identifying patients with an abdominal aortic aneurysm.

BACKGROUND: Abdominal aortic aneurysms (AAA) are often identified incidentally on imaging studies. Patients and/or providers are frequently unaware of these AAA and the need for long-term follow-up. We sought to evaluate the outcome of a nurse-navigator-run AAA program that uses a natural language processing (NLP) algorithm applied to the electronic medical record (EMR) to identify patients with imaging report-identified AAA not being followed actively. METHODS: A commercially available AAA-specific NLP system was run on EMR data at a large, academic, tertiary hospital with an 11-year historical look back (January 1, 2010, to June 2, 2021), to identify and characterize AAA. Beginning June 3, 2021, a direct link between the NLP system and the EMR enabled for real-time review of imaging reports for new AAA cases. A nurse-navigator (1.0 full-time equivalent) used software filters to categorize AAA according to predefined metrics, including repair status and adherence to Society for Vascular Surgery imaging surveillance protocol. The nurse-navigator then interfaced with patients and providers to reestablish care for patients not being followed actively. The nurse-navigator characterized patients as case closed (eg, deceased, appropriate follow-up elsewhere, refuses follow-up), cases awaiting review, and cases reviewed and placed in ongoing surveillance using AAA-specific software. The primary outcome measures were yield of surveillance imaging performed or scheduled, new clinic visits, and AAA operations for patients not being followed actively. RESULTS: During the prospective study period (January 1, 2021, to December 30, 2021), 6,340,505 imaging reports were processed by the NLP. After filtering for studies likely to include abdominal aorta, 243,889 imaging reports were evaluated, resulting in the identification of 6495 patients with AAA. Of these, 2937 cases were reviewed and closed, 1183 were reviewed and placed in ongoing surveillance, and 2375 are awaiting review. When stratifying those reviewed and placed in ongoing surveillance by maximum aortic diameter, 258 were 2.5 to 3.4 cm, 163 were 3.5 to 3.9 cm, 213 were 4 to 5 cm, and 49 were larger than 5 cm; 36 were saccular, 86 previously underwent open repair, 274 previously underwent endovascular repair, and 104 were other. This process yielded 29 new patient clinic visits, 40 finalized imaging studies, 29 scheduled imaging studies, and 4 AAA operations in 3 patients among patients not being followed actively. CONCLUSIONS: The application of an AAA program leveraging NLP successfully identifies patients with AAA not receiving appropriate surveillance or counseling and repair. This program offers an opportunity to improve best practice-based care across a large health system.