Healthcare Directories Workshop Focuses on Secure Validation of Clinician Data

Social Media and Opportunities for Cardiovascular Medicine

The social media platform Twitter allows users to post content up to a limit of 280 characters, the engagement with these posts can be measured by the number of impressions. With all this content available, hashtags (#) are used to group certain posts relating to a certain topic, a popular cardiology hashtag being #CardioTwitter.

In particular, the free open-access medical education (#FOAMed) hashtags have gained significant popularity. By using these hashtags, clinicians of all levels can get involved in discussions and advise from around the world on echocardiograms and angiograms etc. This has also impacted the younger generation of cardiologists as the fellows-in-training (FITs) have also been able to use SoMe to generate a hashtag, #FITSurvivalGuide, which provided access to basic clinical topics. For academic cardiologists, SoMe has also become more prominent in their field. It is now not uncommon for academics to demonstrate the impact of their work digitally.

The opportunity for SoMe to generate real-world connections has brought together different groups in the field from around the world. For example, the Women in Cardiology movement has gained a large following on both Twitter and Facebook. These SoMe platforms have allowed this group to discuss issues of importance affecting the female cardiologist population, including issues with the gender pay gap and work-life balance challenges. Connections have also been made for grassroots cardiology advocacy groups, for example, #GoRedForWomen and #SouthAsianCVD to name a few.

MiHIN Adds to Use Case Portfolio, Creates Interoperability Sandbox

Michigan Health Information Network Shared Services (MiHIN) continues to expand the services it offers statewide, including the creation of a sandbox environment to enable healthcare organizations to simulate and test interoperability scenarios and a recently announced partnership with Care Convene, a telehealth platform.

In a recent interview, Tim Pletcher, MiHIN’s executive director, described the organization’s approach as a “use case factory.” MiHIN has created a methodology to work on bringing new use cases such as admission, discharge and transfer (ADT) notifications into production and add to their portfolio of shared services. “We put the use cases through a pipeline, including working with the state government or commercial payers to align financial or policy incentives. We have had payers involved in every single use case we have,” he stressed.

Once a new use case is in place, MiHIN runs the report cards on how health systems are doing. Are they sending data and how clean is the data and how consistent? “We are like teachers who want everyone to get an A,” Pletcher said. “We send report cards home to Mom and Dad – the payers or the state government. They decide whether they are going to give everyone their allowance. We don’t measure HIE participation, because just being part of a club does not mean you are creating value. But at the use case level, it does. For instance, we have 330 of the long-term care facilities/SNFs sending ADTs in Michigan, which is quite novel, but it is because we were able to create incentives to motivate them to do that.”

To help organizations work through issues with data sharing, MiHIN has a new nonprofit subsidiary called the Interoperability Institute and it has set up a sandbox environment called “Interoperability Land,” described as a shared online environment hosted in the Amazon Web Services Cloud where organizations, developers, and technology providers can engage in simulated interoperability scenarios to develop, test or demonstrate new application capabilities. It uses completely realistic but synthetic patient data.

“We started with immunizations, so people could practice submitting to the state registry,” Pletcher explained. “Very quickly we moved to ADTs and CCDAs, because nobody had any data to start priming the pump and work out all the kinks.”

Report: Most Health Systems Still Lack Long-Term Digital Strategy

Healthcare’s digital transformation is still in the early stages of maturity relative to other sectors, but CIOs do understand the imperative to drive digitalization, according to a new report from advisory firm Damo Consulting.

The report’s findings were revealed in focus group discussions with nearly 40 CIOs and senior health IT leaders who are members of the College of Health Information Management Executives (CHIME). When asked to define digital transformation in healthcare, 60 percent of respondents said it is about using digital technologies to reimagine business processes and customer experiences, while others stated that it means delivering healthcare when, where and how consumers want it; or using data, analytics and artificial intelligence (AI) to improve outcomes.

According to the researchers, today, health systems fall into four key models of digital adoption: reliance on electronic health record (EHR) systems to drive digital engagement (Model 1); digital strategy singularly focused on virtualization of care (Model 2); stand-alone digital initiatives driven by internal demand (Model 3); and strategic investments in long-term digital health platforms (Model 4).

Most health systems, especially smaller and mid-tier ones, operate in Models 1 and 2 and only the nation’s largest health systems are operating in Model 4. The majority of CIOs in the focus group, however, acknowledge that all enterprises need to shift to Model 4, the findings revealed.

“In my discussions with health system CIOs, what emerged is that not only are most health systems in the early stages of adoption, but there is no clear consensus on what digital transformation looks like or how to achieve it,” said Paddy Padmanabhan, CEO, Damo Consulting. “Most health systems consider their EHR system as their digital strategy or are developing standalone solutions on an as-needed basis, without a long-term digital strategy in mind.”

Patient Health Information: Connecting Electronic Medical Records with External Apps

  1. A patient stops by a busy urgent care center for concerning flu-like symptoms. Rather than waiting a couple hours just to start being seen by the nurse, the patient sits at a kiosk and interacts with an artificial intelligence (AI) assistant that asks the patient some questions tailored to the signs and symptoms. The patient can even follow easy instructions to take her own temperature, capture pictures of her ear drums using a digital otoscope and record her heart and lung sounds. The AI system then records this information, infers a probable diagnosis of influenza and sends the information to the clinician’s electronic medical records (EMR) with the Subjective Objective Assessment Plan (SOAP) note mostly filled out. Now, when the patient sees the clinician, most of the work is done and the time can be spent between the patient and clinician discussing the diagnosis and treatment plan.

  2. Another busy patient is on a ranch and has developed a rash. It’s an hour drive to the nearest doctor or urgent care. So instead, the patient uses his phone and sets up a telehealth visit with a doctor right then. The doctor can review the patient’s previous history obtained from the medical record, look at an uploaded picture of the rash sent by the patient and converse with the patient over video. The document note would then be sent back to the EMR along with the rest of the patient’s record. The patient’s regular primary care provider can now access this encounter for follow up.

  3. A busy doctor is seeing 30 patients a day in the clinic. Documentation and order entry is time consuming, but this doctor uses her phone and a special app to record the patient/physician encounter and use natural language processing and machine learning to turn this recording into a SOAP note. In addition, orders for labs and x-rays are captured during the encounter and sent to the EMR along with the SOAP note.

These scenarios are not science fiction. They represent new modalities of capturing patient information outside of the typical workflow—which I will call external apps for now. These modalities are working to be interoperable with the rest of the patient’s health information, which is typically stored in the medical record and claims databases. As their prevalence grows within the health IT ecosystem, it is important to understand how standards are being leveraged to integrate these applications.

Cerner, Duke create Learning Health Network to automate data for research