Association of palliative care and hospital outcomes among solid tumour oncology inpatients.

OBJECTIVES: We aimed to explore the association between receiving an inpatient palliative care consultation and hospital outcomes, including in-hospital death, intensive care unit (ICU) use, discharge to hospice, 30-day readmissions and 30-day emergency department (ED) visits. METHODS: We conducted a retrospective chart review of Yale New Haven Hospital medical oncology admissions from January 2018 through December 2021, with and without inpatient palliative care consultations. Hospital outcome data were extracted from medical records and operationalised as binary. Multivariable logistic regression was used to estimate ORs for the association between number of inpatient palliative care consultations and hospital outcomes. RESULTS: Our sample included 19 422 patients. Age, Rothman Index, site of malignancy, length of stay, discharge to hospice, ICU admissions, hospital death and readmissions within 30 days differed significantly between patients who received versus did not receive a palliative care consultation. On multivariable analysis, receiving one additional palliative care consultation was significantly associated with higher odds of hospital death (adjusted OR=1.15, 95% CI 1.12 to 1.17) and discharge to hospice (adjusted OR = 1.23, 95% CI 1.20 to 1.26), and lower odds of ICU admission (adjusted OR=0.94, 95% CI 0.92 to 0.97). There was no significant association between palliative care consultations and readmission within 30 days or with ED visits within 30 days. CONCLUSION: Inpatients receiving palliative care had increased likelihood of hospital death. However, when controlling for significant differences in patient presentation, patients had nearly 25% greater odds of discharge to hospice and less odds to transition to ICU level of care.

Association Between Postdischarge Medical Oncology Follow-Up Appointments and Downstream Health Care Use: A Single-Institution Experience.

PURPOSE: There is limited understanding of the role of postdischarge medical oncology follow-up during care transition periods. Our study describes the care transition patterns and the association between postdischarge medical oncology appointments and downstream health care use at a tertiary academic center. METHODS: We conducted a retrospective cohort study of 25,135 medical oncology admissions between 2018 and 2020 at Yale New Haven Hospital. We examined the association between postdischarge medical oncology appointment timing with 30-day all-cause readmissions and emergency department (ED) visits using multivariable logistic regression models and propensity score-matched analyses. RESULTS: Compared with admissions without appointment within 30 days, admissions with postdischarge medical oncology appointment within 30 days were associated with lower rates of all-cause 30-day readmission (odds ratio [OR] = 0.56, 95% CI, 0.52 to 0.59; P < .001) and ED visit (OR = 0.56, 95% CI, 0.52 to 0.59; P < .001). Admissions with appointment ≤ 14 days were associated with lower rates of 30-day readmission (OR = 0.28, 95% CI, 0.25 to 0.32; P < .001) and ED visit (OR = 0.56, 95% CI, 0.52 to 0.63; P < .001) compared with those with appointment within 15-30 days. Similar patterns in health care use were seen with propensity score matching. Subgroup analyses of cancer types with the most admissions observed similar trends between 30-day readmission and ED visits with appointment timing. CONCLUSION: Timely postdischarge medical oncology appointments were associated with significantly lower likelihood of 30-day readmission and ED visits, suggesting a potential role for postdischarge follow-up as an intervention to decrease health care use.

Monitoring InBaskets With Feedback to Providers Enhances the Timeliness of Patient Care.

PURPOSE: Electronic health records have changed providers' workflow. Epic's InBasket supplants traditional communication and is a central hub for clinical information. Failure to promptly complete records impairs communication and revenue collection. By tracking providers' InBasket activities and offering feedback, we hoped to improve InBasket management and interdisciplinary communication. METHODS: We created a report to track 273 providers' InBasket activities, including ambulatory transcriptions, chart cosignatures, order cosignatures, patient calls, results, and billable encounters. The report showed how often and for how long each activity was delinquent. We completed three Plan-Do-Study-Act cycles. During cycle 1 (November to December 2015), we sent all providers automated e-mails with their monthly results. During cycle 2 (January to April 2016), we focused solely on billable encounter closure and sent targeted e-mails to providers with > 50 delinquent encounters. The e-mails stated that providers had 30 days to complete encounters or their practices would be closed to new patients; at 30 days, noncompliant providers had 60 days before practice suspension. During cycle 3 (May to September 2016), we continued to monitor and send targeted e-mails to providers who accumulated > 50 encounters. We modeled the financial impact of the intervention using net closure data, the report's aging function, and billing logs. RESULTS: InBasket monitoring with structured feedback decreased open encounters by 53.43%. We did not see improvements in the other metrics that the report tracked. We estimate that $231,724 was saved as a result of the intervention and $349,179 was lost to filing deadlines. CONCLUSION: Automated e-mails did not reduce open encounters; targeted e-mails to providers improved InBasket management.

Electronic Intervention to Improve Structured Cancer Stage Data Capture.

PURPOSE: Cancer staging is critical for prognostication, treatment planning, and determining clinical trial eligibility. Electronic health records (EHRs) have structured staging modules, but physician use is inconsistent. Typically, stage is entered as unstructured free text in clinical notes and cannot easily be used for reporting. METHODS: We created an Epic Best Practice Advisory (BPA) decision support tool that requires physicians to enter cancer stage in a structured module. If certain conditions are met, the BPA is triggered as a hard stop, and the physician cannot chart until staging is complete or a reason for not staging is selected. We used Plan, Do, Study, Act methodology to inform the intervention and compared preexisting staging rates to rates at 4, 8, and 12 months postintervention. RESULTS: For 12 months before BPA implementation, 1,480 of 5,222 (28%) patients had cancer stage structured within the Epic problem list. From 1 to 4 months after the BPA 2,057 of 1,788 (115%) cases were staged in Epic. In the 5- to 8-month period after the BPA, 1,057 of 1,893 (56%) cases were staged, and 9 to 12 months after the BPA 1,082 of 1,817 (60%) were staged. CONCLUSION: Electronic decision support improves the rate of structured cancer staging at our institution. The staging rates between 56% and 60% for the 5- to 8-month and 9- to 12-month periods likely reflect accurate postintervention staging rates, whereas the initial 115% rate for 1 to 4 months is inflated by providers staging cancers diagnosed before the BPA.