Electronic health record (EHR) vendors consistently overrepresent their products as a one stop shop for clinicians with everything needed to manage patient care, but this is simply not true. These vendors claim that the risk scores generated by EHRs are all hospitals need for identifying patients requiring clinical intervention before medical conditions develop. Have you been hearing that from your EHR rep?
They’re wrong. Here’s why:
EHRs weren’t designed to be diagnostic tools. The original intent of the EHR was to support clinical workflows and consolidate clinical documentation. Applying an EHR to a diagnostic use case is going to be no more effective than utilizing the doctor’s ballpoint pen as an imaging instrument. It’s just not what they were made for.
EHRs can’t extend beyond the limits of their own data fields. They can’t ingest data from the myriad of publicly and commercially available sources, such as de-identified patient data, Medicare, clinical research, commercial payor, social, socio-economic, medical literature and census data, to create the underlying mapping needed to generate patient-specific clinical interventions. (And, even if EHRs could incorporate these data, no patients would be comfortable with their EHRs reflecting such detailed and identifiable information.)
EHRs are old technology built for an era before today’s analytics capabilities. EHRs can’t deliver prescriptive analytics. They were not built on an architectural framework that supports artificial intelligence (AI) or machine learning (ML). They’re not capable of in-memory processing to support deep data analytics. The best they can do is generalize across populations to create alarm fatigue for doctors and nurses.
So when it comes to preventing harm — preserving patients’ lives, preventing family grief and saving hospitals untold millions of dollars every year — EHRs simply aren’t good enough.
Hospitals need purpose-built technology designed to do all these things that EHRs can’t. We’re living in the age of analytics. Practitioners today have access to a solution that can augment the judgment and wisdom they apply to patient care with raw processing power and sophisticated AI-enabled analytics. We can pinpoint the patients in most need of timely intervention and with the greatest likelihood of a favorable response to treatment. Soon enough, hospitals that don’t take advantage of AI- or ML-enabled diagnostic tools are going to be considered negligent by way of limiting their care due to outdated technology.
EHR risk scores are designed to identify only the highest risk patients — those who are typically already known to clinicians. Focusing on this patient segment adds to alarm fatigue, underscoring the minimal efficacy of the EHR as a diagnostic solution. By relying solely on EHR risk scores, hospitals are missing “impactable” patients — those patients who fall outside the highest risk group but whose outcomes can be improved by the right intervention.
Jvion offers a different approach: The Jvion CORE’s prescriptive analytics for preventable harm solution pinpoints the impactable patients whose risk trajectory can be changed and provides clinicians with patient-specific recommendations that will drive to a better outcome. Hospitals can apply the Jvion CORE quickly to any of 50 preventable harm vectors (such as sepsis, readmissions, falls, avoidable ER visits, and pressure injuries) without the need to create new models or to have perfect data.
The Jvion advantage over EHR-only approaches is undeniable: Jvion has proven effective in clinical settings for nearly a decade, with hospitals reporting average reductions of 30% in preventable harm incidents and avoidable cost savings of $6.3 million a year.
If you’re a CEO, CFO, CMO, CNO or another practitioner who has an EHR vendor whispering in your ear, don’t be misled to believe that the risk scores provided by the EHRs you already have are adequate for your patients’ and your clinicians’ needs. Talk to us at Jvion. We’ll demonstrate why risk scores just don’t cut it when trying to prevent patient harm.