The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices.
Julian D SchwabSilke D KühlweinRolf HühneHannah SpohnUdo X KaisersHans Armin KestlerPublished in: JMIR medical informatics (2023)
Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient's feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them.
Keyphrases
- patient reported outcomes
- healthcare
- electronic health record
- psychometric properties
- clinical trial
- patient reported
- computed tomography
- public health
- image quality
- dual energy
- big data
- mental health
- randomized controlled trial
- magnetic resonance imaging
- case report
- positron emission tomography
- high resolution
- health information
- end stage renal disease
- data analysis
- machine learning
- magnetic resonance
- pet ct
- artificial intelligence
- study protocol