ABSTRACT
BACKGROUND: The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, researchers must agree on dataset definitions that not only cover all elements relevant to the respective medical specialty but that are also syntactically and semantically interoperable. Following such an effort, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties. OBJECTIVE: To (i) specify a workflow for the development of interoperable dataset definitions that involves a close collaboration between medical experts and information scientists and to (ii) apply the workflow to develop dataset definitions that include data elements most relevant to COVID-19-related patient research regarding immunization, pediatrics, and cardiology. METHODS: We developed a workflow to create dataset definitions that are (i) content-wise as relevant as possible to a specific field of study and (ii) universally usable across computer systems, institutions, and countries, i.e., interoperable. We then gathered medical experts from three specialties (infectious diseases with a focus on immunization, pediatrics, and cardiology) to the select data elements most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications using HL7 FHIR. All steps were performed in close interdisciplinary collaboration between medical domain experts and medical information specialists. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. RESULTS: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected according to the here developed consensus-based workflow by medical experts from the respective specialty to ensure that the contents are aligned with the respective research needs. We defined dataset specifications for a total number of 48 (immunization), 150 (pediatrics), and 52 (cardiology) data elements that complement the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module. CONCLUSIONS: These here presented GECCO extension modules, which contain data elements most relevant to COVID-19-related patient research in infectious diseases with a focus on immunization, pediatrics and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for the development of further dataset definitions. The GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
ABSTRACT
Background The coronavirus disease 2019 (COVID-19) pandemic has underscored the importance of real world data in everyday clinical practice and has highlighted some long-standing problems of our healthcare system such as gaps in primary data collection, hurdles in the evaluation of patient data, and complexity regarding the data exchange between different institutions. In addition, changes in physician-patient relationships such as transitions from a paternalistic to a partnership-based relationship model as well as increasing digitalization have shaped our modern understanding of healthcare, emphasizing the issue of patient autonomy and self-efficacy and highlighting the need for innovative, patient-centered approaches. Methods Using the patient journey as a theoretical construct, we describe the collection of different types of real world data, their meaning and handling. Conclusion Mapping the patient journey process combined with a widely used data standard can lead to the acquisition of primary data in the healthcare sector which can be used by all medical treatment institutions. This will lead to an exchange of valuable data between institutions and circuit the current problem of proprietary formats. Furthermore, the evaluation of patient-reported outcomes as a standard in the clinical routine could enhance patients' autonomy and optimize treatment. Thus, the overall treatment effectiveness and survival of patients can be improved by creating a common data language and using a holistic, human-centered care approach through integrating perspectives of patients and their loved ones.
ABSTRACT
Hintergrund Die COVID-19-Pandemie („coronavirus disease 2019“) hat die Bedeutung von Real World Data (RWD) im klinischen Alltag unterstrichen und die fatalen Folgen von längst existierenden Problemen wie Lücken in der Primärdatenerfassung, Hürden bei der Auswertung von Patientendaten sowie erschwertem Patientendatenaustausch zwischen verschiedenen Einrichtungen nochmal deutlich gemacht. Darüber hinaus haben Entwicklungen weg von einem paternalistischen hin zu einem partnerschaftlichen Modell der Arzt-Patienten-Beziehung sowie die zunehmende Digitalisierung unser Verständnis von Gesundheitsversorgung geprägt, das Thema der Patientenautonomie und Selbstwirksamkeit in den Vordergrund gebracht und den Bedarf an innovativen, patientenzentrierten Lösungsansätzen verdeutlicht. Methoden Wir nutzen die „patient journey“ als theoretisches Konstrukt, entlang dessen wir die Sammlung von verschiedenen Typen von RWD, ihre Bedeutung und Umgang damit beschreiben. Schlussfolgerung Die Abbildung der „patient journey“ in Verbindung mit der Nutzung eines einheitlichen Datenstandards kann zur Erfassung von Primärdaten im Gesundheitswesen führen, die von allen medizinischen Behandlungseinrichtungen genutzt werden können. Dies wird den Austausch von Daten zwischen Einrichtungen erleichtern. Darüber hinaus könnte die fortlaufende Auswertung von patientenberichteten Ereignissen als Standard in der klinischen Routine die Patientenautonomie stärken und die Behandlung optimieren. Zusammenfassend lässt sich sagen, dass der Behandlungserfolg, das Gesamtüberleben und das Wohlbefinden der Patienten durch die Schaffung einer gemeinsamen Datensprache und eines ganzheitlichen, menschenzentrierten Ansatzes verbessert werden können.