BACKGROUND
The COVID-19 pandemic has turned the care model of health systems around the world upside down, abruptly cancelling face-to-face visits to avoid contagion and redirecting the model towards telemedicine. Digital transformation boosts information systems, which, the more robust they are, the easier it is to monitor the healthcare system in a highly complex state and allow for more agile and reliable analysis.
OBJECTIVE
To analyse diagnoses from primary care visits and distinguish between those that had higher and lower variations, relative to the 2019 and 2020 periods (roughly pre- and during COVID), to identify clinical profiles that may have been most impaired and diagnoses least visited during the pandemic.
METHODS
A database from the Primary Care Services Information Technologies Information System of Catalonia is used. The object of the analysis is the register of visits (2,824,185) and their diagnostic codes (3921974, mean 01.38 per visit) as approximators of the reason for consultation, registered according to the International Classification of Diseases (ICD-10) at three different grouping levels. The data is represented by a term frequency matrix and analysed recursively in different partitions aggregated according to date.
RESULTS
In number of visits, the increase in non-face-to-face (+267%) does not compensate for the decrease in face-to-face visits (-47%), with an overall reduction in the total number of visits (-1.36%) despite the notable increase in nursing visits (10.54%). The visits with the largest increase in 2020 are those with diagnoses related to COVID-19 (codes Z20-Z29, 2.540%), along with codes related to economic and housing problems (Z55-Z65, 44.40%). Most among the rest of the codes visited decrease in 2020 relative to 2019. Those that have presented the most important reductions have been some chronic pathologies such as arterial hypertension (I10-I16; -32.73%) or diabetes mellitus (E08-E13; -21.13%), but also obesity (E65-E68; -48.58%) and bodily injuries (T14; -33.70%). Visits with mental health related diagnosis codes have decreased, but less than average. Both for children and adolescents and for adults, there was a decrease in consultations for respiratory infections (J00-J06; -40.96%). The results show very significant year-on-year variations (in absolute terms, an average of 12%), a sign of the strong shock to the health system.
CONCLUSIONS
The disruption in the primary care model in Catalonia has led to an explosion in the number of non-face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity and bodily injuries. Instead, visits for diagnoses related to economic and housing problems have increased, which emphasizes the importance of Social Determinants of Health and the pathway to Population Health Management. The big data-based approaches presented in this analysis, consistent with intuitions from everyday clinical practice, can help inform decision making by health planners in order to use the next few years to focus on the least treated diseases in 2020.
Accessing primary care Big Data: the development of a software algorithm to explore the rich content of consultation records
