Healthcare information technology is undergoing enormous changes with broad consumer impact. One major area of innovation is mobile healthcare or mHealth. mHealth has the potential to provide patients and physician’s with a broad range of interactive tools - the success of which depends on greater effectiveness in standardizing and structuring vocabularies across healthcare. Hence, why this is of great interest to me and our community of practitioners.
MHealth is resulting in new technologies and approaches for healthcare. Classes of application include patient monitoring, remote diagnostics, Rx compliance-monitoring, self-monitoring for wellness, patient tracking, home healthcare, and payment and reimbursement management systems, among others.
The challenges of data management and integration are magnified significantly by mHealth programs and initiatives. This nascent and developing field requires greater numbers of systems and tools to communicate and manage information - as it is, the healthcare IT industry is already a tower of babel of conflicting and confusing standards from MeSH, SNOMED, ICD-9, ICD-10, LOINC and others. This means that further fragmentation from new applications and new entrants into the field will cause problems and challenges to be even more magnified before things settle down into accepted methods for organizing and transmitting data.
Transaction Processing versus Quality of Patient Care
Although there are already standards in use (e.g. ICD-9), particularly for patient procedure and diagnosis coding, these standards have limited utility. They work well for insurance reimbursement but are insufficient to provide a full picture of a patient and the effectiveness of treatment. There is a significant opportunity to use mHealth to accelerate data capture for evidence-based medicine - that is practices that tie clinical decisions to scientifically-trusted data.
In order for some of the expected efficiencies of healthcare reform and data integration through Electronic Health Records (EHR), data needs to be analyzed in new ways that will yield insights into improved, safer, higher quality and more effective treatments. In order to achieve this end, there will need to much greater standardization and structuring of healthcare data to support analysis.
Another area with great promise is in the continuous monitoring of patient conditions and biochemical markers (like blood sugar levels for diabetics). Noninvasive devices embedded in everyday items like articles of clothing will one day allow physicians to see an almost continuous feed of patient data and perform interventions or changes in protocol on a proactive and preventative basis
We can’t begin to describe all of the interesting areas where mHealth will transform our lives.
Taxonomy, Metadata, IA and Patient Experience
Taxonomy and metadata and overall information architecture play a central and pivotal role in these systems and processes. New devices will continue to fragment data and new approaches will require adoption of additional protocols and data standards. In order to make any system user friendly, we need to explore how data is surfaced to the user, whether that person is a physician, medical organization, intermediary, or patient. Humans interact with systems at every step of the process and usability will be paramount to patient engagement and adoption.
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