Letter to the Editor
Use big data as a source of data for health care quality indicators construction
Utilización de big data como fuente de datos para la construcción de indicadores de calidad asistencial
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These indicators would be easy to analyse, provide a comparison with other health centers or hospitals, and improve decision-making efficacy, so that, ultimately, an improvement in the quality of care received by the patient would be reported.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">We agree with the author on the need to measure health outcomes as indicators, and on the use of big data as a tool to generate evidence from the observation of real-world data.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In Spain there are at least 4 clinical databases with which to build health indicators: Minimum Basic Data Set (MBDS), Primary Care Clinical Database (BDCAP), Database for Pharmacoepidemiological Research in the Public Environment (BIFAP) and databases for analysis of the pharmaceutical provision of the National Health System (NHS). All are free and available to health professionals.</p><p id="par0020" class="elsevierStylePara elsevierViewall">The Register of Specialised Healthcare Activity of the Minimum Basic Data Set (RSHA-MBDS) collects administrative and clinical data from each care centre contact, and records the morbidity attended in both public and private hospitals and outpatient centres at a specialised level.</p><p id="par0025" class="elsevierStylePara elsevierViewall">Currently, the RSHA-MBDS uses the International Classification of Diseases version 10 (ICD-10) to standardise health information and it includes demographic data, care contact data, origin, admission and discharge service, type of discharge (home, death, to other hospitals, etc.), data on the procedures and interventions performed, clinical data (principal diagnosis and secondary diagnoses) and costs (through the Related Diagnosis Groups [DRG]).</p><p id="par0030" class="elsevierStylePara elsevierViewall">One of the applications given it by our group has been to calculate the comorbidity of the patients treated and to validate the best comorbidity index that makes it possible to control this variable as a potential confounding factor in the measurement of health outcomes,<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> and also to evaluate adverse reactions associated with drugs used for COVID-19.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">In the primary care (PC) setting, the BDCAP refers to a set of data related to the care provided, collected from medical records, with a large random sample of 10% of the population. It contains 9 types of variables: general information, user administrative data, health problems (according to the International Classification of Primary Care [CIAP] version 2), procedures performed, interconsultations, pharmaceutical prescriptions, clinical parameter results, number of visits and costs. It has made it possible to objectify the cardiovascular comorbidity of patients with diabetes in Spain.<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">For its part, BIFAP is oriented towards research on the patterns of use, safety and effectiveness of medicinal products. It draws on the data recorded by family doctors and primary care paediatricians. It includes the number of visits, diagnoses (coded according to CIAP-BIFAP), imaging tests, remarks noted about the episode, interconsultations, vaccinations, laboratory tests, prescriptions, and adverse reactions. Recently, it made it possible to demonstrate how renin-angiotensin system inhibitors did not increase the risk of COVID-19 infection requiring hospital admission, and therefore should not be suspended.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">Finally, each autonomous community has intelligent analysis tools for NHS pharmaceutical provisions (Business Intelligence - Farm@drid, in the case of the Community of Madrid). These are fed with NHS prescription and prescription billing data, which enables pharmaceutical prescription indicators (medication safety, efficiency, rational use of antibiotics, etc.) to be generated, and to obtain information classified by the prescribing physician and the patients that allows both specialised care and primary care professionals to carry out interventions to guarantee the safe and rational use of medicinal products.</p><p id="par0050" class="elsevierStylePara elsevierViewall">We agree with the author about the need to continue moving forward and include big data tools to automatically extract information elements such as medical ontologies, or natural language processing techniques that allow the measurement of the most valuable quality indicator: real life health outcomes. All the same, the mandatory nature of the four existing databases presented, the high coverage —above 96% in the case of the MBDS registry—, and the application of strict validation and coherence controls between the variables, guarantee the reliability of the data.</p><p id="par0055" class="elsevierStylePara elsevierViewall">In conclusion, with the use of big data support tools which provide contrasted and validated information, the health system will have the real capacity to make decisions and manage processes based on health results.</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Financing</span><p id="par0060" class="elsevierStylePara elsevierViewall">This document has not required any type of financing.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Financing" ] 1 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:5 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Indicadores de calidad para el clínico" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "X. Bayona Huguet" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Med Clín (Barc)." "fecha" => "2022" "volumen" => "159" "numero" => "6" "paginaInicial" => "287" "paginaFinal" => "288" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0010" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Comparison of three comorbidity measures for predicting in-hospital death through a clinical administrative nacional database" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:4 [ 0 => "I. Oterino-Moreira" 1 => "S. Lorenzo-Martínez" 2 => "Á López-Delgado" 3 => "M. Pérez-Encinas" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.3390/ijerph191811262" "Revista" => array:6 [ "tituloSerie" => "Int J Environ Res Public Health." 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