Virtual healthcare models, usually between healthcare professionals and patients, have developed strongly during the coronavirus disease 2019 (COVID-19) pandemic, but there are no data corresponding to models between clinicians. An analysis was made of the impact of the COVID-19 pandemic upon the activity and health outcomes of the universal e-consultation program for patient referrals between primary care physicians and the Cardiology Department in our healthcare area.
MethodsPatients with at least one e-consultation between 2018 and 2021 were selected. We analyzed the impact of the COVID-19 pandemic upon activity and waiting time for care, hospitalizations and mortality, taking as reference the consultations carried out during 2018.
ResultsA total of 25,121 patients were analyzed. Logistic regression analysis showed a shorter delay in care and resolution of the e-consultation without the need for face-to-face care to be associated to a better prognosis. The COVID-19 pandemic periods (2019–2020 and 2020–2021) were not associated to poorer health outcomes compared to 2018.
ConclusionsThe results of our study show a significant reduction in e-consultation referrals during the first year of the COVID-19 pandemic, with a subsequent recovery in the demand for care, and without the pandemic periods being associated to poorer outcomes. The reduction in time elapsed for resolving the e-consultations and no need for face-to-face visits were associated to improved outcomes.
Los programas de telemedicina entre clínico y paciente se han desarrollado con fuerza durante la pandemia de enfermedad por COVID-19, pero no hay datos de experiencias entre clínicos. Nuestro objetivo es analizar el impacto de la pandemia por COVID-19 en la actividad y resultados en salud de un programa de consulta electrónica universal (e-consulta) para todas las derivaciones de pacientes entre médicos de atención primaria y el Servicio de Cardiología de nuestra área.
MétodosAnalizamos mediante regresión logística 25,121 pacientes con al menos una e-consulta entre 2018 y 2021 realizada con el Servicio de Cardiología de nuestra área sanitaria. También se realizó el análisis de regresión logística del impacto de la pandemia por COVID-19 sobre la resolución de la e-consulta y tiempo de espera de la atención, hospitalizaciones y mortalidad, tomando como referencia las consultas realizadas durante 2018.
ResultadosObservamos que una menor demora en la atención y resolución de la e-consulta (sin necesidad de atención presencial) se asociaba a un mejor pronóstico. Los períodos de pandemia COVID-19 presentaron similares resultados a los del 2018.
ConclusionesLos resultados de nuestro estudio muestran una significativa reducción de las derivaciones a través de e-consulta durante el primer año de la pandemia por COVID-19 con recuperación posterior de la demanda asistencial sin que los períodos de pandemia se asociasen con peores resultados en salud. La reducción del tiempo de demora de resolución de la e-consulta y el grupo sin necesidad de consulta presencial se asociaron a un mejor pronóstico.
The coronavirus disease (COVID-19) pandemic began to affect our healthcare system in early 2020, and the virus expanded within the country over the following months, causing a national state of emergency declaration on 16 March 2020.1 An exponential increase in pandemic-related healthcare services was observed, with a parallel reduction of healthcare for patients with both acute and chronic non-pandemic conditions.2–4 In addition, it proved necessary to provide non-COVID-19 healthcare to preserve the safety of patients and healthcare professionals.5,6
These changes inherent to the pandemic resulted in less care activity destined to chronic diseases in primary care (PC)7 and a reduction in cases seen in Cardiology Departments, in relation to both follow-up and complementary tests and interventional procedures,4 as well as a decrease in emergency room visits and admissions.8
This situation raised the need to develop healthcare management models and platforms allowing quality healthcare without the need for displacement of patients and caregivers, and has resulted in the publication of numerous experiences in the transformation of healthcare.9–11 Evaluation of the safety of these programs is crucial, and they must at least allow the provision of healthcare with quality indicators similar to those of face-to-face care, in addition to affording electronic medical records integrated among healthcare levels. This would provide an ideal scenario for the development of such programs.
Practically all virtual healthcare experiences describe communication programs between patients and healthcare professionals, though very few publications have addressed the usefulness of virtual communication models between clinicians (e-consultation from clinician to clinician), particularly those integrated within an electronic clinical history model. Our group is a pioneer in the development of a universal e-consultation program, integrated within the clinical histories in our region (Autonomous Community) in Spain, for all patient referrals by PC physicians to Cardiology Departments.12 We have described the characteristics and results of our e-consultation program,12 introduced in 2013. Its implementation has benefited patient prognosis in relation to the previous face-to-face care period (2010–2012), in addition to reducing healthcare time and improving accessibility.12,13
The objective of the present study is to analyze the impact of the COVID-19 pandemic upon the healthcare activity (delay time and resolution of the interconsultation) and health outcomes (emergency room visits, hospitalization and mortality) of the universal e-consultation program in relation to patient referrals between PC physicians and the Cardiology Department, as a first step in the organization of outpatient care. In addition, we seek to evaluate the association between resolution of the e-consultation without the need for face-to-face care and the delay in resolution, and the health outcomes.
Material and methodsPatientsThe Cardiology Department of our Integrated Healthcare Area provides coverage for 446,603 inhabitants and receives an average of 35 referrals a day from primary care (PC) at three hospital centers — one of which is a district hospital. The healthcare area includes 301 family physicians distributed in 25 primary care departments, with 56 primary care centers and 21 peripheral clinics. Since 2013, outpatient care is organized through electronic consultation (e-consultation) between physicians, where the PC physician summarizes the clinical data of the patient justifying referral and the cardiologist moreover has access to all the complementary tests performed on the patient (blood tests, electrocardiogram, chest X-rays, etc.). Based on this information, the cardiologist may resolve the request telematically, recording his/her assessment in the clinical history directly, or may establish an appointment with the patient in a single event face-to-face consultation. At this visit, the physical examination and all the complementary tests required to establish a diagnosis are made. The patient is subsequently discharged and the consultation is considered resolved, or the patient is included in one of the follow-up programs of the Cardiology Department12 (Fig. S1).
On a routine basis, e-consultations are resolved in three-month rotations of 6 cardiologists in the Department, with each cardiologist responding to an average of 5 daily e-consultations. Patients in whom face-to-face consultation is considered necessary are appointed in specific agendas attended by three cardiologists who preferentially perform this type of care activity.12 During the pandemic, in 2020, e-consultation was essentially resolved by 12 cardiologists, all of whom had experience in responding to e-consultations, given the rotational nature of the organization in the Department since 2013.
The present study involves an analytical observational design to analyze the clinical variables and results of one-year of follow-up of the referred patients in three years: 2018 (considered to be the reference period, as they were not affected by the pandemic in either referral or follow-up); 2019 (not affected by the pandemic in terms of referral but affected in relation to follow-up); and 2020 (affected in terms of both referral and follow-up). We also included the patient care data during 2021, though without the one-year follow-up data being yet available. In total, a sample of 25,121 patients was obtained.
The study was approved by the Ethics Committee of our Healthcare Area on 23 March 2022 with reference 2021/496.
Study variablesEpidemiological information (gender and age) and the patient personal history were recorded. To assess the patient prognosis in each of the periods, the following events were considered: hospital admission (total and cardiovascular [CV]) and mortality (total and CV), at one year after e-consultation by the Cardiology Department. Cardiovascular-related hospital admissions included ischemic heart disease, heart failure, cerebrovascular events, peripheral arterial disease, atrial fibrillation (AF), and other arrhythmias. Cardiovascular mortality included ischemic heart disease, heart failure, cerebrovascular events, and peripheral arterial disease.
The records relating to hospital admissions were obtained from ICD-10 coding, and the COVID-19 admissions were thus available. However, mortality was obtained based on patient public health card cancelations and civil registry mortality data, and in this case we did not know the COVID-19 deaths, since this disease was not contemplated in the coding.
Statistical analysisQualitative variables are reported as percentages (%), and quantitative variables as the mean and standard deviation (SD). The chi-squared test and analysis of variance (ANOVA), respectively, were used for hypothesis tests between the analytical periods. To analyze the prognostic impact of the periods, as well as of the consultation modality, multivariate analysis was used, with logistic regression allowing assessment, along with each period examined, of the impact of epidemiological (age, gender), clinical (history of hypertension, diabetes mellitus, ischemic heart disease, heart failure, atrial fibrillation, cerebrovascular disease and peripheral arterial disease) and healthcare variables (number of emergency room visits, time to resolution of the e-consultation, and form of resolution of the e-consultation) for each of the four events. The odds ratio (OR) and 95% confidence interval (95%CI) were calculated in each case.
The SPSS version 22.0 statistical package was used throughout.
ResultsResults of the study sampleFig. 1 shows the number of referrals in each trimester from 2018 to 2021 corresponding to the 25,121 patients included in the analysis. We observed a significant decrease in referrals in the second trimester of 2020, at which time the state of emergency in Spain was decreed, with the suspension of all non-essential activities.
Table 1 shows the clinical-epidemiological characteristics of the patients in the study sample at the time of referral from PC. The gender distribution was similar in the analyzed groups (p = 0.817), with a significantly older age in the case of the patients referred in 2021 (p < 0.001). The prevalence of cardiovascular risk factors was similar in the four periods. However, the prevalence of all cardiovascular diseases was progressively higher in the years 2020 and 2021, with the exception of cerebrovascular disease (p = 0.391) and peripheral arterial disease (p = 0.570).
Baseline clinical-epidemiological characteristics and healthcare data of the patients in the sample in the four analyzed periods.
| Total | 2018 | 2019 | 2020 | 2021 | p-Value | |
|---|---|---|---|---|---|---|
| 25,121 | 5960 | 6282 | 5466 | 7413 | ||
| Females (%) | 49.3 | 49.2 | 49.7 | 49.3 | 48.9 | 0.817 |
| Age (years, mean [SD]) | 64.7 (18.2) | 64.4 (18.2) | 64.0 (18.6) | 64.7 (18.1) | 65.5 (17.9) | <0.001 |
| Personal history | ||||||
| Arterial hypertension (%) | 54.1 | 53.6 | 54.6 | 54.0 | 54.1 | 0.765 |
| Diabetes mellitus (%) | 18.6 | 18.0 | 18.6 | 18.9 | 18.8 | 0.569 |
| Overweight-obesity (%) | 25.7 | 25.0 | 25.8 | 26.2 | 25.9 | 0.466 |
| Cardiovascular disease (%) | 19.1 | 17.5 | 17.5 | 20.1 | 21.1 | <0.001 |
| Ischemic heart disease (%) | 9.6 | 8.4 | 8.6 | 10.8 | 10.5 | <0.001 |
| Heart failure (%) | 8.1 | 7.2 | 7.4 | 8.1 | 9.4 | <0.001 |
| Atrial fibrillation (%) | 20.8 | 19.2 | 19.1 | 22.0 | 22.5 | <0.001 |
| Cerebrovascular disease (%) | 0.8 | 0.7 | 0.9 | 0.7 | 0.8 | 0.391 |
| Peripheral arterial disease (%) | 3.3 | 3.3 | 3.2 | 3.6 | 3.3 | 0.570 |
| e-consultation resolution | ||||||
| e-consultation resolves (%) | 61.1 | 61.3 | 64.2 | 56.6 | 61.7 | <0.001 |
| 1 single face-to-face visit (%) | 25.8 | 25.8 | 24.2 | 28.1 | 25.4 | |
| 1 or more successive visits (%) | 13.2 | 12.9 | 11.6 | 15.3 | 12.9 | |
| Healthcare activity | ||||||
| Delay (days, mean [SD]) | 7.8 (9.6) | 8.0 (6.8) | 11.5 (6.8) | 5.0 (4.4) | 6.9 (12.9) | <0.001 |
| No. of cardiological tests (1 year) (number, mean [SD]) | 0.76 (1.13) | 0.76 (0.93) | 0.63 (0.89) | 0.75 (1.17) | <0.001 | |
In terms of care (Table 1), e-consultation proved less resolutive in 2020, with a need for more face-to-face and follow-up consultations among the patients referred during that period. Nevertheless, in that year the delay was reduced, with delays throughout 2021 being shorter than in 2018 and 2019, i.e., prior to the pandemic and which we can take as reference. The number of tests performed in Cardiology, as well as the emergency room visits during the year after e-consultation, decreased among the patients referred in 2019 and 2020.
Results of the hospital admission prognostic analysisThe incidence of hospital admissions increased progressively over the years, being higher in patients referred in the year 2020 (both total and CV hospital admissions). The incidence of COVID-19 hospitalizations among patients referred to the Cardiology Department was 3.4%, with ischemic heart disease being the main cause of admission (9.3%), followed by heart failure (7.8%) and atrial fibrillation (6.5%) (Table 2).
Characteristics of the hospitalizations and one-year mortality among the referred patients in the analyzed periods.
| Total | 2018 | 2019 | 2020 | 2021 | p-Value | |
|---|---|---|---|---|---|---|
| 25,121 | 5960 | 6282 | 5466 | 7413 | ||
| Hospitalizations | ||||||
| Total hospitalizations first year (%) | 12.3 | 12.1 | 11.8 | 13.3 | 0.040 | |
| CV hospitalizations first year (%) | 5.5 | 5.1 | 5.2 | 6.2 | 0.016 | |
| Cause of hospitalization | ||||||
| Ischemic heart disease (%) | 9.3 | 7.6 | 10.8 | 9.4 | <0.001 | |
| Heart failure (%) | 7.8 | 7.4 | 6.6 | 9.5 | ||
| Atrial fibrillation (%) | 6.5 | 6.4 | 6.2 | 5.5 | ||
| COVID-19 (%) | 0.1 | 3.3 | ||||
| Valve disease (%) | 3.2 | 3.2 | 2.8 | 3.4 | ||
| Cancer (%) | 4.7 | 5.1 | 4.7 | 4.3 | ||
| Ischemic stroke (%) | 1.2 | 1.7 | 1.5 | 0.4 | ||
| Embolic stroke (%) | 0.7 | 0.8 | 0.4 | 0.8 | ||
| Hemorrhagic stroke (%) | 0.7 | 0.4 | 0.8 | 0.8 | ||
| Decompensated COPD (%) | 1.1 | 1.5 | 1.1 | 0.8 | ||
| Respiratory infection (%) | 3.4 | 4.4 | 3.1 | 2.6 | ||
| Other causes (%) | 61.4 | 61.5 | 61.9 | 59.2 | ||
| Mortality | ||||||
| Mortality first year (%) | 2.5 | 2.5 | 2.2 | 3.2 | 0.002 | |
| Cardiovascular mortality first year (%) | 1.1 | 1.2 | 0.8 | 1.3 | 0.022 | |
| Causes of death | <0.001 | |||||
| Heart failure (%) | 8.9 | 10.4 | 7.3 | 6.9 | ||
| Ischemic heart disease (%) | 8.2 | 8.7 | 8.0 | 8.4 | ||
| Ischemic stroke (%) | 4.5 | 4.1 | 5.0 | 4.2 | ||
| Cancer (%) | 20.7 | 21.2 | 22.1 | 21.0 | ||
| Respiratory infection (%) | 2.0 | 2.8 | 2.3 | 0.4 | ||
| Peripheral vascular disease (%) | 1.5 | 1.5 | 2.5 | 0.4 | ||
| Diabetes mellitus (%) | 1.5 | 2.4 | 1.3 | 1.1 | ||
| Valve disease (%) | 0.7 | 1.3 | 0.8 | 0.0 | ||
| Atrial fibrillation (%) | 0.7 | 1.3 | 0.5 | 0.0 | ||
| Hemorrhagic stroke (%) | 0.2 | 0.4 | 0.3 | 0.0 | ||
| Other causes (%) | 51.1 | 45.9 | 49.9 | 57.6 | ||
The percentages of causes of hospitalizations refer to the total hospital admissions.
Both the total and CV hospitalizations increased with patient age (OR: 1.02 [1.01–1.02] and 1.02 [1.01–1.02], respectively), and were less frequent in females. Diabetes and atrial fibrillation were associated with an increase in both types of hospital admission. Ischemic heart disease was associated with an increased risk of CV hospitalization (Table 3).
Multivariate analysis of the influence of the variables upon the study sample prognosis.
| CV hospitalization | Total hospitalization | CV mortality | Total mortality | |
|---|---|---|---|---|
| OR (95%CI) | OR (95%CI) | OR (95%CI) | OR (95%CI) | |
| Age | 1.02 (1.01–1.02) | 1.02 (1.01–1.02) | 1.10 (1.08–1.12) | 1.10 (1.08–1.11) |
| Gender | ||||
| Male (ref.) | 1.00 | 1.00 | ||
| Female | 0.79 (0.68–0.90) | 0.74 (0.68–0.82) | 0.76 (0.56–1.02) | 0.55 (0.46–0.67) |
| Comorbidities | ||||
| Arterial hypertension | 1.06 (0.91–1.24) | 1.05 (0.94–1.17) | 0.90 (0.64–1.27) | 0.82 (0.66–1.02) |
| Diabetes mellitus | 1.25 (1.07–1.46) | 1.16 (1.04–1.30) | 0.85 (0.61–1.19) | 1.19 (0.97–1.46) |
| Ischemic heart disease | 1.61 (1.36–1.91) | 1.13 (0.98–1.29) | 1.66 (1.17–2.36) | 1.36 (1.07–1.72) |
| Heart failure | 1.19 (0.98–1.45) | 1.16 (0.99–1.33) | 3.17 (2.31–4.34) | 2.43 (1.96–3.01) |
| Atrial fibrillation | 1.53 (1.32–1.78) | 1.23 (1.10–1.37) | 1.40 (1.04–1.89) | 1.40 (1.15–1.70) |
| Cerebrovascular disease | 1.24 (0.89–1.74) | 1.04 (0.80–1.33) | 1.79 (1.06–3.02) | 1.01 (0.66–1.53) |
| Peripheral arterial disease | 1.26 (0.95–1.66) | 1.13 (0.91–1.39) | 2.49 (1.62–3.83) | 2.10 (1.55–2.86) |
| Cardiology assistance | ||||
| Emergency room (1 year) | 1.15 (1.12–1.19) | 1.34 (1.31–1.38) | 1.13 (1.07–1.19) | 1.30 (1.24–1.35) |
| Reduction of delay resolution of e-consultation | 0.98 (0.97–0.99) | 0.98 (0.97–0.99) | 0.95 (0.93–0.99) | 0.98 (0.96–0.99) |
| e-consultation resolves (ref.) | 1.00 | 1.00 | 1.00 | 1.00 |
| One face-to-face visit | 1.58 (1.27–1.96) | 1.12 (0.99–1.27) | 1.45 (1.07–1.97) | 1.71 (1.40–2.10) |
| Follow-up face-to-face visits | 5.20 (4.24–6.37) | 1.94 (1.72–2.19) | 1.13 (0.77–1.65) | 1.76 (1.36–2.29) |
| Year of care | ||||
| 2018 | 1.00 | 1.00 | 1.00 | 1.00 |
| 2019 | 0.83 (0.72–0.97) | 0.94 (0.85–1.03) | 0.86 (0.59–1.26) | 1.17 (0.92–1.48) |
| 2020 | 0.99 (0.83–1.19) | 1.04 (0.92–1.18) | 1.09 (0.76–1.55) | 1.17 (0.92–1.49) |
Multivariate logistic regression analysis in which all variables shown in the table were entered in the model. OR: odds ratio; 95%CI: 95% confidence interval.
The bold/italicized values are statistically significative.
The one-year total and CV mortality rate after e-consultation increased in 2020 (p = 0.002 for total mortality, and p = 0.022 for CV mortality), following a decreasing trend in the previous years. In all three periods, cancer was the leading cause of mortality, followed by heart failure and ischemic heart disease (Table 2).
The prognostic analysis showed that mortality increased with age and was lower in women in both mortality analyses. A history of cardiovascular disease (ischemic heart disease, heart failure, atrial fibrillation, peripheral vascular disease and peripheral arterial disease) was associated to an increased risk of both total and CV mortality in all cases (Table 3).
Results of the care modelResolution of demand for care through e-consultation without the need for face-to-face consultation, and the reduction of waiting time to resolve e-consultations, were associated to a significant decrease in the risk of both forms of hospitalization and mortality (Table 3).
We observed an association between the need for emergency care and an increased risk of hospitalization and mortality (total and CV) (Table 3).
Care provided through our e-consultation program over the two years of the pandemic (2019 and 2020) was not independently associated to increased hospitalization and mortality rates (total and CV) (Fig. 2).
Discussion
The results of our study show a significant decrease in patient referrals through e-consultation by PC physicians to a Cardiology Department during the first year of the COVID-19 pandemic, followed by subsequent recovery of the demand for care, and without the pandemic periods (2019–2020 and 2020–2021) being associated to poorer health outcomes versus the pre-pandemic period (2018–2019). Likewise, delays in e-consultation resolution were significantly lower during the pandemic, with more than 50% of the referrals being resolved without the need for face-to-face consultation. This group of patients was associated with a better prognosis at one year in all three periods analyzed (2018–2019, 2019–2020 and 2020–2021). On the other hand, the lesser delay in e-consultation resolution was independently associated to an improved prognosis in patients with a mean care demand resolution time of 7.8 days.
To our knowledge, this is the first description of the impact of the COVID-19 pandemic upon the characteristics and health outcomes of an e-consultation program between clinicians (PC physicians and cardiologists from a concrete healthcare area). In addition to reducing the on-site care burden and avoiding unnecessary travel for patients and caregivers, our results could be useful for organizing the management of care demand referrals from PC physicians to a Cardiology Department. Early assessment by a cardiologist of the reason for consultation by the PC physician allows patient risk stratification, identifying those individuals requiring rapid and specific intervention in order to prevent disease progression that could condition the need for emergency care and increase the risk of hospitalization and mortality — without the health scenario conditioned by the COVID-19 pandemic affecting the health outcomes. Our results show that a healthcare model with good health outcomes evidenced in the years before the pandemic12,13 is able to maintain these outcomes, despite the fact that its organization was not modified during the pandemic.
The COVID-19 pandemic constitutes an important challenge for healthcare systems that have been forced to transform their services in a short period of time.14 Both inpatient care and outpatient care must be modified and adapted to allow for the continuity of care. Telemedicine, particularly virtual healthcare contacts with patients at home, has in some cases made it possible to maintain such continuity of care.14–16 The persistence of the COVID-19 pandemic and the progressive occurrence of viral mutations require healthcare systems to innovate in order to adapt to a changing healthcare scenario, with the aim of addressing new challenges and seeking to maintaining excellence in patient care.17,18
Over the last two years, multiple studies have described and evaluated the results of various telemedicine programs introduced following the start of the COVID-19 pandemic in different healthcare systems and patient groups.8,10 A positive impact upon the recovery of continuity of care has been reported in most cases using virtual medicine models connecting patients and healthcare professionals.19 However, to date no clinical practice guidelines have been introduced including telemedicine in the care of patients with cardiovascular diseases, and previous publications have reported wide variability in the acceptance and use of telemedicine in different specialties, care levels and patient groups.20 In this regard, more research is needed to guide healthcare system organization managers in implementing sustainable, patient need-focused and cost-effective telemedicine projects enabling equity in healthcare delivery and based on health outcomes.21 In particular, healthcare systems need to implement telemedicine projects that allow care to reach those patients that are most vulnerable and with poorer access to healthcare.22 This applies particularly to patients living in areas far away from healthcare centers, older people and patients with limitations in accessing the technologies that make the different telemedicine programs possible.23,24 The implementation of such strategies thus requires teamwork involving healthcare managers and other sectors such as technology service providers and social organizations.25 The expansion of telemedicine services requires public/private investment and collaboration among all sectors involved in the implementation of such programs.26
Telemedicine projects allowing direct communication between healthcare professionals – usually hospital and primary care physicians – are not subject to the limitations described above. However, very few publications have described such experiences or the results regarding accessibility to healthcare and patient prognosis versus exclusively face-to-face care models, both before and during the COVID-19 pandemic.18 Although different modalities have been described, e-consultations conducted through an electronic clinical history integrated between care levels and performed with predefined time intervals, appear to be a good strategy for organizing outpatient care demand in Cardiology. It has been shown to identify patients who do not require on-site care, and to improve health outcomes as compared to on-site care for all referrals. Our group has recently described the results regarding healthcare accessibility with our e-consultation model compared to the previous period of exclusively face-to-face patient care. The data evidence a clear increase in the accessibility and equity of healthcare for older patients (especially those over 80 years of age) — particularly in the healthcare areas furthest from the hospital.13 The results we present herein reinforce these findings, as they describe the strengths of e-consultation during the COVID-19 pandemic period.
Study limitationsThere are certain limitations in the analysis of our results. However, the experience described in a large cohort of patients with demographic, clinical and prognostic information integrated within an electronic medical record, reinforces the clinical and health management relevance of our findings for addressing outpatient care demand consultations from primary care physicians to a Cardiology Department during the first two years of the COVID-19 pandemic.
There is potential bias in the information derived from the analysis of retrospective data, with limited access to the causes of mortality, though the records referred to all deaths occurring during our follow-up period were available. On the other hand, it was not possible to identify the healthcare contacts of the patients with professionals outside the public health system of our area, and this could also have influenced our results to some extent. Nevertheless, the impact of the above must be very small, given the scant presence of private care systems in our healthcare area (8 points below the national average).
ConclusionsThe results of our study show a significant decrease in patient referrals through e-consultation by PC physicians to a Cardiology Department during the first year of the COVID-19 pandemic, followed by subsequent recovery of the demand for care, and without the pandemic periods being associated to poorer health outcomes versus the immediately preceding pre-pandemic period. Likewise, delays in e-consultation resolution were significantly lower during the pandemic, with more than 50% of the referrals being resolved without the need for face-to-face consultation. This group of patients was associated with a better prognosis at one year in all three periods analyzed. On the other hand, the lesser delay in e-consultation resolution was independently associated to an improved prognosis in patients with a mean care demand resolution time of 7.8 days. Our results could be useful for organizing the management of care demand referrals from PC physicians to a Cardiology Department.
Ethical considerationsThe present study was approved by the Ethics Committee of Santiago de Compostela and Lugo on 23 March 2022, with reference 2021/496.
FundingThe authors state that they have received no funding for this article.
Conflicts of interestNone declared.
The following are the supplementary data to this article:
