Productivity improvements have helped a wide range of industries – with the exception of the healthcare industry. From 1999 to 2014, productivity in the healthcare sector increased by only 8%, while other industries achieved much greater efficiency gains of 18%. While comparisons of productivity across industries tend to be inaccurate, they do show Health care lags far behind other industries in terms of productivity and capabilities.
To improve productivity in healthcare in practice, two things must happen. First, data must be understood as a strategic asset. The data must be leveraged through intelligent and end-to-end workflow solutions, as well as the use of artificial intelligence (AI) – driving automation and placing the patient at the center of the imaging value chain.
Second, to be able to talk about the value chain at all, the areas of competency must be linked. Communication should be as smooth, open and secure as possible. The goal is to ensure that all relevant data is available when needed by patients, healthcare professionals, and medical researchers alike.
A modern enterprise imaging software solution should prioritize improved outcomes, improved diagnostics, and enhanced collaboration.
Healthcare Today: Gaps, Bottlenecks, Silos
The costs and consequences of the current fragmented state of far-reaching healthcare data: operational inefficiencies and unnecessary duplication, treatment errors, and missed opportunities for basic research. Recent medical literature is replete with examples of missed opportunities—and patients at risk from a lack of data sharing.
More than four million Medicare patients are discharged to skilled nursing facilities (SNFs) each year. Most are elderly patients with complex conditions, and transmission can be risky. According to a 2019 study published in American Journal of Managed Care, one of the main reasons patients perform poorly during this transition is the lack of sharing of health data—including information that is missing, late, or difficult to use—between hospitals and SNFs. Poor transitional care practices between hospitals and SNFs Compromising quality and safety outcomes for this population,” the researchers note.
Even within hospitals, data sharing remains a huge problem. A 2019 American Hospital Association A study published in the journal Health Care Analyzed interoperability functions that are part of Enhance interoperability The program is administered by the American Centers for Medicare and Medicaid Services (CMS) and approved by eligible US hospitals. The study showed that, of 2,781 non-federal acute care hospitals, only 16.7% had adopted all six essential functions required to meet the objectives of the Phase 3 Certified Electronic Health Record Technology (CEHRT) program. Data interoperability in healthcare is not a natural issue.
Data warehouses and incompatible data sets remain another obstacle. In an article published in 2019 in the magazine JCO Clinical Cancer InformaticsAnd the The researchers analyzed data from the Cancer Imaging Archive (TCIA), and specifically studied nine lung and brain research datasets containing 659 data fields in order to understand what was needed to align data for access across studies. The effort took more than 329 hours over six months, simply to identify 41 data fields nested in three or more files, and align 31 of them.