Over the past decade, we’ve spent billions to digitize healthcare. Health IT was to bring us the same exponential efficiency gains that computers and the Internet brought nearly every other industry. But now that rooms of paper have transitioned into rooms of servers and swarms of software vendors attempt to surf the wakes of legacy EHRs, the acute impact of this stoic transition begin to appear. Some of these newly diagnosed alignments are approaching risk of epidemic.
I am writing this to discuss our findings from a 300-vendor study attempting to understand the root causes and, most importantly, the prevention measures individuals can take when confronted with known early symptoms.
Type 1 and 2 MU (further mutations into Type 3)
An early stage MU diagnosis was a catalyst to much of the following conditions. In 2009, it first appeared in populations incentivized to spread it via certified EHR technology. If caught early, although not curable, it could have been contained and controlled. However, it soon became chronic and subsequently categorized as type 2. And it looks now as though a more progressive mutation is afoot, growing beyond incentivized to penalized attestation.
Hyperactive Click Finger
Most commonly affecting the right index finger, hyperactive click finger (HCF) resulted from premature adoption of EHRs as spurred by type 1 MU. Market driven adoption would have controlled click counts to safe levels as sovereign end users would have chosen vendors based on efficiency gains,rather than subsidy. A regimen of optimization efforts led by EHR therapists is a potential solution that some patients have found effective. However, these therapies are usually administered at extremely high hourly costs and repeated consults are inevitable.
Acute Alert Fatigue
As MU progressed to type 2, clinical decision support combined with CPOE brought on acute alert fatigue in provider populations. This is commonly misdiagnosed as Bipolar Disorder or mild Tourette’s. Comorbidities frequently include HCF. EHR vendors have backed off heavy alerts and periphery vendors are beginning to set precedence with FDA clearance for forceful support. Additionally, alerts are normally hard-coded based on known errors and omissions, thus avoiding opportunity for proactive machine learning.
An infectious disease has been uncovered: I14Y Virus (interoperability influenza). Red blood cells clump together and bind the virus to infected cells, making it extremely difficult to share data between inhabitants. Additionally, the inconsistencies in data models create often insurmountable barriers for new software entrants that could otherwise bring increased efficiency and quality. New therapies, including acronyms like FHIR and SMART, are beginning to change public perception of the disease, yet it is still unclear to most of us what the heck they actually mean. Private middle layers are starting up to tackle known I14Y opportunities and a race to the cure is among us. The cure standard will be defined by what is adopted, not what is agreed upon in committees.
Patients and providers are affected by hyperportalitis similarly. Yet it affects each population quite differently. Upon surfacing symptoms, patients simply disengage, causing aggregated MU. Affected providers, under mandate to comply, simply write usernames and passwords on sticky notes under keyboards, or in severe cases, on the frames of their computer screens. This exacerbates conditions leading to potential risk of HIPAAppendicitis.
Despite repeat training videos depicting hospital elevators polluted with oral PHI leaks, we still run a high population risk of HIPAAppendicitis. This creates risk-averse symptoms of committee meeting purgatory and sluggish adoption of innovative cloud-based software therapies.
This is by no means a comprehensive study. I welcome review from my distinguished peers who subscribe to this journal, as well as subsequent research and inquiry. There will be an open comment period prior to the amendment of ICD-10.
(Originially published on HIStalk.)