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healthcare-compass-icon The Opportunity

Not all medications work the same way for every patient. One cancer patient may respond well to a drug, while others don’t respond at all. For some, the drug may even have adverse effects.

But what if healthcare organizations could use patients’ genetic information to prescribe the medications that work for them? And what if that genetic information was included in a permanent health record, and always available to any doctor who works with them - now or in the future?

That sort of targeted medicine is becoming a reality; genomic information for individual patients is becoming affordable, and healthcare organizations are hoping to use this information to better treat and prescribe medications for patients.

Doing so, however, may mean changing workflows and the way healthcare information systems incorporate genomic information into a patient’s electronic medical record.

To study this, and the ways in which physicians react to this sort of precision medicine, a group of healthcare organizations coordinated by a major university, worked together to perform a multisite test of the implementation of genomic medicine.

This group took part in a research project to study pharmacogenomics, the science of using a patient’s genetic information, or genome, to select the right medication for them. As genomic information becomes more affordable and easier to access, it’s important to understand how that information can be used, and how the healthcare industry will adjust to this sort of precision medicine.

During this project, researchers embedded genetic information within electronic health records (EHRs) in five diverse healthcare environments to show that it is not only achievable but also can alter physician behavior toward a vision of personalized medicine.

healthcare-compass-icon Introduction to the Research Project

This research project, which ran approximately four years, focused on two use cases of pharmacogenomics:

  • the selection of the correct anti-platelet medication in cardiovascular patients
  • selecting the most effective drug therapy for non-small cell lung cancer

The project's goals were threefold:

  • To develop, maintain and disseminate genome-informed clinical decision support within adopter sites
  • To select and prospectively genotype patients within adopter sites for subsequent genotype-tailored therapy
  • To evaluate the impact of integrating genetic data and clinical decision support into adopter site EHRs

healthcare-suitcase-icon Site Involvement

One of the healthcare organizations in the network is a large, Wisconsin-based healthcare group, with several hospitals, outpatient clinics, labs and pharmacies in the Midwest.

The group’s lead investigator is heavily involved in research focused on the most commonly prescribed antiplatelet drug. He developed a clinical decision support tool that interfaces with the healthcare group’s EHR system to assist physicians in prescribing the most effective heart attack and stroke prevention medications based on the patient’s genotype.

For this trial, the lead research scientist developed a system that streamlines orders for genetic testing and automates results. Those genetic test results are processed through an algorithm within the EHR which then provides physicians with a prescription tailored to the patient, thus enhancing treatment efficiency and safety.

Creating the system was a huge undertaking, requiring input from departments across the organization. The healthcare organization needed to bring together data and personnel from its IT department, clinicians, researchers and the laboratory in order to conduct the study. For one of the biggest private employers in the region, this was no small task.

Adding to the list of challenges, was that all of the teams needed to communicate with each another - pathologists, molecular lab personnel, members of various IT teams and members of the lab’s IT - were siloed. So, for example, while the lead investigator had no problem communicating with the clinical team, he struggled to get the project’s IT teams the information they needed to build the platform and make the research project happen.

The cutting-edge nature of the project also pushed the limits of what the group’s Healthcare Information Systems (HIS) were able to do. The infrastructure was in place, but the team struggled to break down the project into digestible tasks the various groups involved in the study could implement.

healthcare-bubble-icon Introduction of Omni

Because the project required the creation of a workflow as well as cooperation between several IT departments within the healthcare group’s system, Omni was brought into the project in an advisory capacity by the lead investigator, who had worked with Omni on a previous project.

Omni’s consultants had worked with HIS previously, and not only understood how to communicate with the IT team members, but also understood the subject matter of the research study.

healthcare-lightbulb-icon The Solution Omni Provided

Omni's contribution included:

  • Translating project needs into technical tasks necessary to be completed by the healthcare group’s IT department, which required an understanding of EHRs
  • Developing seamless communications that allowed creation of a direct electronic link for ordering and receiving genetic test results
  • Helping with the design of workflows and algorithms for physicians to use when ordering genetic tests and prescription drugs
  • Developing a data extraction tool that allowed analysis of outcomes based on physician prescription patterns

Omni’s team used an iterative process and input from both the clinical and IT teams to create a process flow that brought in information from all the necessary teams. This process included making changes to information systems and workflows in the lab so, for example, lab results could flow directly into the system.

This new workflow was important because it would help shape the platform by which patients could receive recommendations about which medications they should and should not take based on their genetic information.

One of the challenges encountered by the team was the way data was often entered into the system by clinicians. Often doctors plugged in patient information in the form of a narrative, which made it hard for the platform to extract the necessary data. Omni designed a solution that allowed the platform to pull discrete data from a block of text.

The platform integrates data from labs, doctors and the genomic information in patients’ electronic medical records to make recommendations about medications to prescribing doctors.

It also combines data from different sources - the lab, electronic medical records and outside sources like the FDA - to provide just-in-time guidance to doctors who are sitting with a patient, ready to prescribe. Real time alerts appear on screen if a medication isn’t compatible with a patient’s genetics.

 healthcare-clipboard-iconConclusion

The clinical decision support tool was implemented in early 2018 at one of the healthcare organization’s medical centers in Wisconsin. In the first four months after implementation, more than 300 genetic test results indicated about 60 patients may have benefited from an alternative to clopidogrel.

The study, which began in 2015, wrapped up in 2018. The results of the study will likely be published by the lead investigator in 2019.

The platform itself, however, is now in daily use. Dozens of cardiologists use it in their daily work, prescribing drugs to patients based on their genetic information.

healthcare-target-icon Omni's Impact

Throughout this project, Omni’s consultants were able to create a bridge between the IT team, researchers and doctors to help put this important healthcare study into practice. Omni’s unique role on this project provided the opportunity to showcase the many talents of the team – from translating technical projects to workflow development or data analysis tools, Omni was able to contribute valuable resources to the project and elevate the relationship.

 

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