Automation is one of the biggest buzzwords in business and IT today, and for good reason. As we move into the era of Industry 4.0
, big data, Artificial Intelligence (AI), and the Internet of Things (IoT) are enabling advancements that were unimaginable in years past. However, while manufacturing and technology progress at breakneck speeds, many healthcare processes that are seemingly prime candidates
for automation continue to be done manually.
This isn’t because healthcare decision-makers are averse to change or unaware of the possibilities, but rather because healthcare is a unique industry with a unique set of challenges. There are regulations and requirements healthcare organizations must navigate that other industries never have to think about. Many of the inner workings of IT systems in a healthcare organization are inherently different than “standard” IT infrastructures. All this comes together to add layers of complexity and make the integrations that could enable automation in healthcare difficult to achieve, despite the fact healthcare organizations are full of mundane, repetitive, data-entry intensive work processes that are prime automation candidates.
In this piece, we’ll review the main drivers of complexity limiting healthcare integrations, explain how Olive
is unique in that it was built specifically to help automate healthcare work processes, and review some of the benefits of implementing intelligent automation in healthcare.
Drivers of healthcare integration complexity
As anyone in the industry will tell you, healthcare is complicated. The healthcare industry is different from other industries for a number of reasons. At a high level, two of the biggest drivers of the complexity of healthcare system software: data integration challenges and unique security and compliance requirements.
Here we will discuss those in more detail and dive into why this is the case.
Data integration challenges
One of the main problems with healthcare
is the lack of standardization and consistency between EMR systems. For example, across different systems there can be different ways to do something as simple as identify a patient. This is because EMR systems were built with the intent to be secure and reliable, but interoperability was an afterthought.
This has lead to a scenario where a significant amount of human time and effort is spent manually moving data from one system to another. Where automation is possible, it is often based on APIs (Application Programming Interfaces) or HL7 (Health Level 7) streams that are difficult to integrate and often lack all the information needed to complete a given work process. In addition to HL7, some of the other standards, formats, and databases those working with healthcare data “in the wild” may encounter include:
- FHIR (Fast Healthcare Interoperability Resources)
- NCPDP (National Council for Prescription Drug Programs) SCRIPT
- ICD (International Classification of Diseases)-9&10
- LONIC (Logical Observation Identifiers Names and Codes)
- NPI (National Provider Identifier)
- SNOMED CT (Systematized Nomenclature of Medicine — Clinical Terms)
..and more. As you can imagine, this makes getting data from point A to point B problematic without compromising the integrity of the data a daunting task. For this reason, processes like eligibility checks
, claims processing
, and other data-entry heavy tasks that would seem prime candidates for automation in other verticals, are labor-intensive tasks in the healthcare sector. While each of these formats serves a purpose and has some upside on its own, often being created to improve standardization, solve problems with older standards, or meet new requirements; taken as a whole they aren’t always conducive to interoperability. The end result of a myriad of well-intentioned standards is a number of different systems within a healthcare facility using different standards. This leads to difficulties tying everything together costs a significant amount of time and resources.
Just how bad is the problem is the data integration problem? To try and quantify the scope, consider that HealthData Management
reported that data integration issues cost health and human services agencies $342 billion.
Drilling down further, let’s consider what seems to be a very preventable problem that has cost the industry billions: denials. Denials cost hospitals and health systems over $262 billion annually
, and over 60% of these denials are related to missing information. This statistic is at least in part symptomatic of the consequences of tasking people with transferring data across multiple, discrete systems. Human error and oversights are bound to occur. There are too many potential points of failure in today’s EMR systems and data entry work processes.