A clinician-to-clinician universal electronic consultation programme at the cardiology department of a Galician healthcare area improves healthcare accessibility and outcomes in elderly patients.

Aims: We aimed to assess longer-term results (accessibility, hospital admissions, and mortality) in elderly patients referred to a cardiology department (CD) from primary care using e-consultation in outpatient care. Methods and results: We included 9963 patients >80 years from 1 January 2010 to 31 December 2019. Until 2012, all patients attended an in-person consultation (2010-2012). In 2013, we instituted an e-consult programme (2013-2019) for all primary care referrals to cardiologists that preceded a patient's in-person consultation when considered. We used an interrupted time series (ITS) regression approach to investigate the impact of e-consultation on (i) cardiovascular hospital admissions and mortality. We also analysed (ii) the total number and referral rate (population-adjusted referred rate) in both periods, and (iii) the accessibility was measured as the number of consultations and variation according to the distance from the municipality and reference hospital. During e-consultation, the demand for care increased (12.8 ± 4.3% vs. 25.5 ± 11.1% per 1000 inhabitants, P < 0.001) and referrals from different areas were equalized. After the implementation of e-consultation, we observed that the increase in hospital admissions and mortality were stabilized [incidence rate ratio (iRR): 1.351 (95% CI, 0.787, 2.317), P = 0.874] and [iRR: 1.925 (95% CI: 0.889, 4.168), P = 0.096], respectively. The geographic variabilities in hospital admissions and mortality seen during the in-person consultation were stabilized after e-consultation implementation. Conclusions: Implementation of a clinician-to-clinician e-consultation programme in outpatient care was associated with improved accessibility to cardiology healthcare in elderly patients. After e-consultations were implemented, hospital admissions and mortality were stabilized.

Longer-Term Results of a Universal Electronic Consultation Program at the Cardiology Department of a Galician Healthcare Area.

BACKGROUND: Telemedicine models play a key role in organizing the growing demand for care and healthcare accessibility, but there are no described longer-term results in health care. Our objective is to assess the longer-term results (delay time in care, accessibility, and hospital admissions) of an electronic consultation (e-consultation) outpatient care program. METHODS: Epidemiological and clinical data were obtained from the 41 258 patients referred by primary care to the cardiology department from January 1, 2010, to December 31, 2019. Until 2012, all patients were attended in an in-person consultation (2010-2012). In 2013, we instituted an e-consultation program (2013-2019) for all primary care referrals to cardiologists that preceded patients' in-person consultations when considered. We used an interrupted time series regression approach to investigate the impact of the e-consultation on (1) delay time (days) in care and (2) hospital admissions. We also analyzed (3) total number and referral rate (population-adjusted referred rate) in both periods (in-person consultation and e-consultation), and (4) the accessibility was measured as number of consultations and variation according to distance from municipality and reference hospital. RESULTS: During the e-consultation, the demand increased (7.2±2.4% versus 10.1±4.8% per 1000 inhabitants, P<0.001), and referrals from different areas were equalized. The reduction in delay to consultation during the in-person consultation (-0.96 [95% CI, -0.951 to -0.966], P<0.001) was maintained with e-consultations (-0.064 [95% CI, 0.043-0.085], P<0.001). After the implementation of e-consultation, we observed that the increasing of hospital admission observed in the in-person consultation (incidence rate ratio, 1.011 [95% CI, 1.003-1.018]), was stabilized (incidence rate ratio, 1.000 [95% CI, 0.985-1.015]; P=0.874). CONCLUSIONS: Implementing e-consultations in the outpatient management model may improve accessibility of care for patients furthest from the referral hospital. After e-consultations were implemented, the upward trend of hospital admissions observed during the in-person consultation period was stabilized with a slight downward trend.

Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study.

BACKGROUND: The prognosis of patients with COVID-19 infection is uncertain. We derived and validated a new risk model for predicting progression to disease severity, hospitalization, admission to intensive care unit (ICU) and mortality in patients with COVID-19 infection (Gal-COVID-19 scores). METHODS: This is a retrospective cohort study of patients with COVID-19 infection confirmed by reverse transcription polymerase chain reaction (RT-PCR) in Galicia, Spain. Data were extracted from electronic health records of patients, including age, sex and comorbidities according to International Classification of Primary Care codes (ICPC-2). Logistic regression models were used to estimate the probability of disease severity. Calibration and discrimination were evaluated to assess model performance. RESULTS: The incidence of infection was 0.39% (10 454 patients). A total of 2492 patients (23.8%) required hospitalization, 284 (2.7%) were admitted to the ICU and 544 (5.2%) died. The variables included in the models to predict severity included age, gender and chronic comorbidities such as cardiovascular disease, diabetes, obesity, hypertension, chronic obstructive pulmonary disease, asthma, liver disease, chronic kidney disease and haematological cancer. The models demonstrated a fair-good fit for predicting hospitalization {AUC [area under the receiver operating characteristics (ROC) curve] 0.77 [95% confidence interval (CI) 0.76, 0.78]}, admission to ICU [AUC 0.83 (95%CI 0.81, 0.85)] and death [AUC 0.89 (95%CI 0.88, 0.90)]. CONCLUSIONS: The Gal-COVID-19 scores provide risk estimates for predicting severity in COVID-19 patients. The ability to predict disease severity may help clinicians prioritize high-risk patients and facilitate the decision making of health authorities.