The health histories of some 20 million people in Kansas and Missouri, compiled in “comprehensive health records,” will be shared across state lines, according to a recent data sharing agreement.
The deal between the Missouri Health Connection (MHC), and the Kansas Health Information Network (KHIN) and subsidiary KAMMCO Health Solutions (KMS), will aggregate data from both exchanges — creating a new comprehensive medical record.
The new insights will lead to quicker treatments, reduce redundant testing and procedures, and improve coordination and decision-making, according to the agencies.
Some experts told Inside Digital Health™ that the partnership is a net win for patients and caregivers, as well.
“The connection of the KHIN and MHC networks solves many challenges with the exchanging of electronic health data today,” said Laura McCrary, Ed.D., executive director of KHIN and president and CEO of KHS. “Patients’ medical records will be electronically available to their physicians and other healthcare providers any time of day. This is critically important as there are times a patient may not be able to communicate all of their health history to their physician or hospitalist in an emergency.”
The new records will be created using a “private and secure technology” producing a record that is longitudinal, and updated electronically and in real-time, according to officials. KAMMCO, doing business as SHINE of Missouri, is physician-led and has partnered with the Missouri State Medical Association.
Calling it an “epic win for Missouri, Kansas and the Midwest,” officials said the deal will affect a majority of patients in both states.
MHC’s network extends to more than half of the in-patient care in Missouri, through 75 hospitals, several hundred clinics and 14 community health centers.
KHIN’s reach in Kansas extends to more than 125 hospitals, nearly three-quarters of the physician practices, as well as pharmacies, home health providers, health plans and long-term-care facilities.
“Making a connection to each other was a sound way for MHC and KHIN to demonstrate our commitment to serving the healthcare providers in our respective networks,” said Angie Bass, president and CEO of MHC. “Data sharing between MHC and KHIN dramatically increase the value of health information exchange to our healthcare customers.”
McCrary told Inside Digital Health™ by phone from the Strategic Health Information Exchange Collaborative conference in Washington, D.C. that both exchanges create interfaces from the EHRs, to the secure interchange platform. HL7 (Health Level Seven) V.2 is the most common data transport method to build the data feeds for labs, notes and other categories. The CCD and ADT data feeds are held in a central data repository, and when a doctor or healthcare provider needs information, then their records system queries the repository, either automatically or through a manual portal.
Now, when either of the queries comes in, it will query the other state, McCrary said.
Kate Shamsuddin, M.S., the senior vice president of strategy at Definitive Healthcare, a Massachusetts-based healthcare data and analytics firm, said that both KHIN and MHC use some of the most common HIE systems – making it much easier to combine forces. Both entities also have a history of partnering with other organizations to expand access to data, she said.
It’s likely to be a net win for patients, added Shamsuddin.
“According to Definitive Healthcare data, Missouri Health Connection currently serves a large number of rural hospitals in the Midwest, so this partnership will also help streamline remote care cases by allowing rural providers to quickly discover the patient record and deliver the appropriate care,” she said. “In rural areas, removing any barriers, particularly when dealing with time-sensitive events is absolutely crucial.”
KAMMCO’s health analytics and information exchange services are also used in Connecticut, New Jersey, Georgia, South Carolina and Louisiana.
Personal digital health profiles show promise in a step-wise approach to chronic disease prevention, according to research published in the journal BMC Public Health.
Of the 22% of patients advised to get a health check at their general practitioner, almost all of them (19%) did. And of the nearly 25% of patients advised to schedule an appointment for behavior-change counseling at their municipal health center, 21% took the advice.
Participants who had fair or poor self-rated health, a body mass index above 30, low self-efficacy, were female, non-smokers or who led a sedentary lifestyle were more likely to attend targeted preventive programs.
A Danish research team implemented a step-wise approach in the Danish primary care sector for the systematic and targeted prevention of chronic disease.
The researchers designed an early detection and prevention intervention for Type 2 diabetes mellitus, cardiovascular disease and chronic obstructive pulmonary disease (COPD). The intervention had two elements:
- General intervention. This involved the creation of a personal digital health profile for each individual in the study population.
- Targeted intervention. This intervention included a health check at the general practitioner or behavior-change counseling at a municipal health center. The targeted interventions were for patients who were deemed likely to benefit from such interventions due to their high overall risk of the chronic conditions or because they regularly engaged in health-risk behaviors.
More than 8,800 patients between the ages of 29 and 60 from 47 general practitioners participated in the study.
Participants received a digital invitation and consent form prior to the study.
The aims of the digital health profiles were centered on four key ideas:
- To motivate and enable patients who otherwise would not have taken up a targeted intervention like the one offered.
- To motivate and enable patients with poor self-management skills to take up the targeted intervention.
- To guide patients with good self-management skills to change their own behavior.
- To keep the healthy and low-risk population from demanding unnecessary health checks from their general practitioner.
Digital health profiles contained clear and concise personalized health information and recommendations for further action. Recommendations included advice to take up a targeted preventive program, facts about health-risk behavior, information about the positive impact of behavior-change and a personalized list of available and relevant behavior-change interventions.
Researchers created the digital health profiles based on the patients’ electronic health records and questionnaire information, which included health-risk behaviors, family history of disease, early symptoms of COPD and osteoarthritis.
Participants were then stratified into one of four groups.
The first group consisted of patients who had treatment for hypertension, hyperlipidemia, Type 2 diabetes mellitus, cardiovascular disease and/or COPD at their general practitioner. The patients in this group did not have any additional intervention beyond usual care.
Patients in the second group were those would likely benefit from a health check at their general practitioner determined by three risk algorithms for the chronic conditions. These patients were advised to schedule a check with their practitioner, which included a medical examination and subsequent health counseling session.
In the third group were patients who were not flagged by the risk algorithms but had a body mass index above 35 and/or reported they regularly engaged in health-risk behavior. Risky behaviors included daily smoking, high-risk alcohol consumption, unhealthy dietary habits and sedentary leisure time activities. Patients in this group were advised to schedule a 15-minute telephone-based counseling session. These could be requested online through the digital health profile.
Patients with a healthy lifestyle and no need for further intervention made up the fourth group.
Deciphering the Findings
Women and participants with sedentary leisure behavior were more likely to attend a health check at their general practitioner. General practitioner attendance rates revealed that physical activity was the strongest predictor of attendance. The attendance for those with sedentary behavior was 28%, while those who exercised during down time was 17%.
Of those who had fair or poor self-related health, 20% of smokers attended the telephone-based counseling session and 42% of the non-smokers attended.
Overall, the attendance rate for patients who were advised to schedule a health check and for those who were advised to schedule a counseling session was near 20%.
“This study suggests that a personal digital health profile may help foster a more equitable uptake of preventive programs in the primary care sector — especially among patients with lower self-efficacy and fair to poor self-related health,” the authors wrote.
The researchers suggest that further research is needed on personal digital health profiles.
The premise of clinical decision support (CDS) software is clear: Use technology to leverage the power of big data to improve patient care and, theoretically, drive down costs. But the technology is, in many ways, still at the starting gate, in part due to technical and bureaucratic hurdles and a lack of scientific data surrounding its use.
Now, a major academic medical center, the University of Virginia Health System, is launching a concerted effort to find ways to integrate CDS into its organization. The evaluation is part of a larger effort at the health system to boost value-based care. It could prove to be a model to help other medical centers take the leap.
Joseph Wiencek, Ph.D., an assistant professor of pathology at UVA, said the health system is looking at products developed in house and elsewhere.
“We have several rules built from our internal analytics/informatics teams but would like to see if there is any value in external support that is becoming more widely available,” he told Inside Digital Health™. “These decision support tools would likely target high-volume, low-cost tests as well as high cost, low volume.”
The team remains in the evaluation and “data-crunching” phase, but he said one important element of the evaluation will be to get buy-in from the health system stakeholders.
“Since we are an academic teaching hospital, it is important to research and strategically partner with service lines and department leads to make sure we achieve lateral buy-in from our colleagues,” he said.
He and teammate Andrew Parsons, M.D., MPH, an assistant professor of medicine at UVA, recently wrote about how reduce laboratory costs, noting that “low-value care” — care that could be eliminated without harming patient safety — costs the U.S. healthcare system an estimated $800 billion each year.
They wrote that integrating decision support tools into electronic health record software could help reduce unnecessary costs. However, given the relative novelty of these types of products, health systems should evaluate them carefully before integration.
Wiencek told Inside Digital Health™ that there simply isn’t much in the way of scientific literature when it comes to the effects of CDS.
“I think the lack of peer-reviewed literature is an enormous impact,” he said.
While the technical work that’s required to implement the systems can be difficult, Wiencek said scientific evaluation will also be key.
“It truly is a multi-modal approach, and support from your colleagues and evidence-based literature will really be the only way these tools will succeed,” he said.
Ultimately, the success of any decision support technology will be about more than just cost. For some institutions, that could be the benchmark. But for others, victory might resemble patients receiving the right test at the right time, he said.
In fact, a recent study by researchers at the Massachusetts Institute of Technology found that CDS helped providers make more appropriate decisions but didn’t result in cost savings, in part because sometimes the most appropriate decision was a high-cost test.
Wiencek said while cost is a major issue in healthcare generally, he doesn’t think people should overemphasize its importance. Besides, he said, the cost implications of better decisions might not appear in the short term.
“Doing the right test for the right patient will lead to better clinical decisions, and patients will get the care that they need or be diagnosed faster,” he said. “If this happens, costs will fall, too. I’d like to see these types of tools lead us in that direction.”
Wiencek offered no timeline as to when UVA will complete its evaluations, though he said the team is proceeding at a “steady pace.” The health system is currently working with a single external vendor, though he said they have been approached by several others.
The U.S. Food and Drug Administration (FDA) has released a letter in support of open data sharing through efforts like the Patient Safety Movement Foundation’s Open Data Pledge, according to an announcement today.
While the FDA did not sign the Open Data Pledge, which is meant for companies, it supports the principles of it.
“We encourage policymakers, healthcare entities including hospitals, digital health technology companies, medical device manufacturers and others to share data to support patient safety,” Jeffrey Shuren, M.D., J.D., director of the FDA Center for Devices and Radiological Health, wrote in a letter to Joe Kiani, founder of the Patient Safety Movement Foundation, which aims to eliminate preventable deaths.
The Center for Devices and Radiological Health supports data sharing to protect patients and promote public health, Shuren noted. He wrote that openly sharing data with patients, providers and researchers could:
- Empower patients to participate in the development and evaluation of medical devices that meet their needs
- Facilitate medical device surveillance and help identify and prevent adverse effects
- Increase the FDA’s knowledge of the benefits and risks of technologies, which could enhance patient safety
When individuals and companies sign the Open Data Pledge, they agree to allow anyone who wants to improve patient safety to interact with their products and access the data that are collected. The agreement is subject to privacy laws.
“We are grateful for FDA’s recognition of our work and thank the nearly 100 enlightened companies that have signed the Open Data Pledge,” Kiani said. “Patient harm can be avoided with predictive algorithms and decision support using data from the myriad of products that touch the patient.”