Productivity improvements have helped a wide range of industries, with the exception of the healthcare industry. From 1999 to 2014, productivity in the health care sector increased by only 8%, while other industries achieved much greater efficiency gains of 18%. Although productivity comparisons between industries tend to be inaccurate, they show that health care lags far behind other industries in productivity and potential.
To operationally improve healthcare productivity, two things must happen. First, data must be understood as a strategic asset. Data should be harnessed through smart, end-to-end workflow solutions, as well as the use of artificial intelligence (AI) to drive automation and put the patient at the center of the healthcare value chain. imagery.
Second, to be able to speak of a value chain, the areas of competence must be connected. The connection should be as transparent, open and secure as possible. The goal is to ensure that all relevant data is available when needed for patients, healthcare professionals and medical researchers.
A modern enterprise imaging software solution should prioritize optimizing outcomes, improving diagnostics, and improving collaboration.
Health care today: gaps, bottlenecks, silos
The costs and consequences of the current fragmented state of healthcare data are significant: operational inefficiencies and unnecessary duplications, processing errors and missed opportunities for basic research. The recent medical literature is replete with examples of missed opportunities and patients being put at risk due to a lack of data sharing.
More than four million Medicare patients are discharged to skilled nursing facilities (SNF) each year. Most of them are elderly patients with complex conditions, and the transition can be dangerous. According to a 2019 study published in the American Journal of Managed Care, one of the main reasons patients fare poorly during this transition is the lack of sharing of health data, including missing, delayed, or difficult-to-use information, between hospitals and SNFs. “Weak transitional care practices between hospitals and NFS compromise quality and safety outcomes for this population,” the researchers noted.
Even within hospitals, data sharing remains a major problem. A 2019 study from the American Hospital Association published in the journal Health care reviewed the interoperability features that are part of the Promoting Interoperability program, administered by the US Centers for Medicare & Medicaid Services (CMS) and adopted by eligible US hospitals. The study showed that among 2,781 non-federal acute care hospitals, only 16.7% had adopted the six core features required to meet Stage 3 Certified Electronic Health Record Technology (CEHRT) goals. from the program. The interoperability of data in the field of health is not obvious.
Data silos and incompatible datasets remain another obstacle. In a 2019 article in the journal JCO Cancer Clinical Informatics, Researchers analyzed data from the Cancer Imaging Archive (TCIA), looking specifically at nine lung and brain research datasets containing 659 data fields to understand what would be needed to harmonize data for the access to cross-sectional studies. The effort took more than 329 hours over six months, just to identify 41 overlapping data fields in three or more files, and to harmonize 31 of them.