Healthcare utilization and adverse outcomes stratified by sex, age and long-term care residency using the Alberta COVID-19 Analytics and Research Database (ACARD): a population-based descriptive study.

BACKGROUND: Understanding the epidemiology of Coronavirus Disease of 2019 (COVID-19) in a local context is valuable for both future pandemic preparedness and potential increases in COVID-19 case volume, particularly due to variant strains. METHODS: Our work allowed us to complete a population-based study on patients who tested positive for COVID-19 in Alberta from March 1, 2020 to December 15, 2021. We completed a multi-centre, retrospective population-based descriptive study using secondary data sources in Alberta, Canada. We identified all adult patients (≥ 18 years of age) tested and subsequently positive for COVID-19 (including only the first incident case of COVID-19) on a laboratory test. We determined positive COVID-19 tests, gender, age, comorbidities, residency in a long-term care (LTC) facility, time to hospitalization, length of stay (LOS) in hospital, and mortality. Patients were followed for 60 days from a COVID-19 positive test. RESULTS: Between March 1, 2020 and December 15, 2021, 255,037 adults were identified with COVID-19 in Alberta. Most confirmed cases occurred among those less than 60 years of age (84.3%); however, most deaths (89.3%) occurred among those older than 60 years. Overall hospitalization rate among those who tested positive was 5.9%. Being a resident of LTC was associated with substantial mortality of 24.6% within 60 days of a positive COVID-19 test. The most common comorbidity among those with COVID-19 was depression. Across all patients 17.3% of males and 18.6% of females had an unplanned ambulatory visit subsequent to their positive COVID-19 test. CONCLUSIONS: COVID-19 is associated with extensive healthcare utilization. Residents of LTC were substantially impacted during the COVID-19 pandemic with high associated mortality. Further work should be done to better understand the economic burden associated with related healthcare utilization following a COVID-19 infection to inform healthcare system resource allocation, planning, and forecasting.

Improving Wait Times and Patient Experience Through Implementation of a Provincial Expedited Diagnostic Pathway for BI-RADS 5 Breast Lesions.

BACKGROUND: Long diagnostic intervals following abnormal breast imaging (DI) cause patient anxiety and possibly poorer prognosis. This study evaluates the effect of a provincial diagnostic pathway for BI-RADS 5 lesions on wait times and the patient-reported experience (PRE). METHODS: With multidisciplinary input, we developed a pathway for BI-RADS 5 lesions featuring expedited biopsy, early surgical referral, and nurse (RN) navigator support. Key diagnostic intervals were captured prospectively and compared with a prepathway control cohort. PRE data were obtained from a voluntary survey. RESULTS: 1205 patients were managed on the BI-RADS 5 pathway with 797 primary care physicians, 57 imaging centers, and 2 regional breast programs participating. Median duration from DI to biopsy was 6 days, from biopsy to pathology report was 5 days, DI to surgical referral was 6 days, and DI to surgical consult was 21 days. Compared with 128 prepathway controls, median intervals from DI to surgical referral and consult were significantly improved (15 vs. 6 days, 26 vs. 21 days, p < 0.001). Amongst 294 women who completed the survey, 92% experienced ≥ 1 anxiety complaint during assessment; prompt surgical consultation and multiple features of RN support reduced anxiety, and wait time satisfaction was high (70%). Patient preferences varied for receiving biopsy results from a surgeon (57%) vs. another provider (43%). CONCLUSIONS: A diagnostic pathway for BI-RADS 5 lesions reduced wait times and improved the patient experience through prompt surgical referral and RN navigator support. Differing preferences for receiving biopsy results emerged, and future iterations should incorporate individualized patient wishes.

Same-Day Surgery for Mastectomy Patients in Alberta: A Perioperative Care Pathway and Quality Improvement Initiative.

BACKGROUND: Same-day surgery (SDS) following mastectomy is safe and well accepted. Overnight admission in patients fit for discharge is an inefficient use of health resources. In response to a national review highlighting SDS following mastectomy at 1.4% in Alberta, a perioperative pathway was conceived. METHODS: The pathway was implemented across Alberta at 13 hospitals beginning in 2016. A steering committee was assembled, and clinical and administrative leads at each site were identified. Opportunities along the patient care experience whereby action could be taken to promote uptake of SDS were identified. Provincially branded support materials including presentations, order sets, and standard operating procedures were developed. Nurse educators provided in-service teaching such as standardized drain care and discharge teaching. Educational booklets, group classes, and online resources were developed for patients and families. An audit of SDS rates, unscheduled return to the emergency department (ED), and readmission rates was reported to teams quarterly, allowing for iterative modifications. Patient-reported experience measures (PREMs) were collected. RESULTS: SDS following mastectomy increased from 1.7 to 47.8%, releasing an estimated 831 bed days per year. No differences in unexpected return to the ED or readmission to hospital existed between SDS patients and those admitted overnight. A total of 102 patients completed the PREM survey, of whom 90% felt "excellent or good" with the plan to go home, how to care for themselves once home, and who to contact should issues arise. CONCLUSIONS: Implementation of a provincial perioperative pathway improved uptake of SDS following mastectomy and demonstrated favorable PREMs.

Machine learning risk estimation and prediction of death in continuing care facilities using administrative data.

In this study, we aimed to identify the factors that were associated with mortality among continuing care residents in Alberta, during the coronavirus disease 2019 (COVID-19) pandemic. We achieved this by leveraging and linking various administrative datasets together. Then, we examined pre-processing methods in terms of prediction performance. Finally, we developed several machine learning models and compared the results of these models in terms of performance. We conducted a retrospective cohort study of all continuing care residents in Alberta, Canada, from March 1, 2020, to March 31, 2021. We used a univariable and a multivariable logistic regression (LR) model to identify predictive factors of 60-day all-cause mortality by estimating odds ratios (ORs) with a 95% confidence interval. To determine the best sensitivity-specificity cut-off point, the Youden index was employed. We developed several machine learning models to determine the best model regarding performance. In this cohort study, increased age, male sex, symptoms, previous admissions, and some specific comorbidities were associated with increased mortality. Machine learning and pre-processing approaches offer a potentially valuable method for improving risk prediction for mortality, but more work is needed to show improvement beyond standard risk factors.

Using administrative data to determine rates of surgical site infections following spinal fusion and laminectomy procedures.

OBJECTIVE: Surgical site infections (SSIs) are a serious and costly post-op complication. Generating SSI rates often requires labor-intensive methods, but increasing numbers of publications reported SSI rates using administrative data. METHODS: Index laminectomy and spinal fusion procedures were identified using Canadian Classification of Health Interventions (CCI) procedure codes for inpatients and outpatients in the province of Alberta, Canada between 2008 and 2015. SSIs occurring in the year postsurgery were identified using the International Classification of Diseases, 10th Revision, Canada (ICD-10-CA) diagnosis and CCI procedure codes indicative of post-op infection. Rates of SSIs and case characteristics were reported. RESULTS: Over the 8-year study period, 21,222 index spinal procedures were identified of which 12,027 (56.7%) were laminectomy procedures, with 322 SSIs identified, an SSI rate of 2.7 per 100 procedures. Of the 9,195 (43.3%) fusion procedures, 298 were identified as an SSI, an SSI rate of 3.2 per 100 procedures. This study found SSI rates increased from 2008 and 2015, and rates were the highest in the 0-18 year age group. CONCLUSIONS: The rates reported in this study were similar to published SSI rates using traditional surveillance methods, suggesting administrative data may be a viable method for reporting SSI rates following spinal procedures. Further work is needed to validate SSIs identified using administrative data by comparing to traditional surveillance.