When physicians get the right kind of alert in an electronic health record—and actually follow its recommendation—it could result in fewer complications and lower costs among hospitalized patients, according to a new study.
Published in the American Journal of Managed Care, researchers from Cedars-Sinai Medical Center and Optum Advisory Services teamed up to examine alerts that popped up on physician computer screens inside their EHR system when their care instructions deviated from evidence-based guidelines.
Those alerts were based on the 'Choosing Wisely' initiative in which different specialties have identified common tests and procedures in their respective areas of expertise that may not benefit patients and should be avoided.
Looking at nearly 26,500 inpatient admissions at Cedars-Sinai Medical Center between October 2013 and July 2016, the researchers studied whether the treating physician followed either all or none of the Choosing Wisely guidance. In 6% of visits, physicians followed all of the triggered alerts and in 94% of visits, physicians followed none of the alerts. In particular, they examined data in which one more of the 18 most frequent alerts were triggered.
For patients whose physicians did not follow the alerts, the odds of complications increased by 29% and the risk of readmissions within 30 days of the patients' original visit was 14% higher. There was also a 6.2% increased length of stay and an additional 7.3% or more than $900 per patient increase in costs after adjusting in differences in patient complexity.
To be clear, the study was examining a product created by Stanson Health, a spinout company founded by Cedars-Sinai executives and in which the health system is a major shareholder.
Optum is a licensed reseller of Stanson's Health's Choosing Wisely alert content and, for the study, married Stanson's data with its own analytics on cost and complications and length of stay for those same admissions.
“We said, ‘Hey, let’s look and see if we can demonstrate that we’re not only able to get docs to look at alerts but we can actually change the ultimate outcomes we’re trying to change like complications and readmissions," John Kontor, M.D., an executive vice president at Clinovations within Optum told FierceHealthcare in an interview. "For us, it was risk-free but it was very risky for Stanson."
It worked out by showing the association they were looking for, he said.
But there are limitations to the study, they acknowledge.
There was no way to measure the impact of specific alerts on outcomes to see if one was more significant than others. There is also no way to know if providers who were more likely to follow alerts may also be more engaged around quality and efficiency in how they practice day to day, Kontor said.
More study is needed to more closely examine the direct impact of the alerts, officials said. But, they said, the study shows using tools could have a real impact on costs and quality.
"The next step is to look at the characteristics of overall alerts and say 'We see a positive impact. Now let's look at what’s most effective about clinical decision support in order to maximize the positive impact on both for patients and providers," said Anne Wellington, a co-author of the study and managing director of the Cedars-Sinai accelerator.
Virginia recently launched its emergency department care coordination (EDCC) program to connect all emergency departments in the Commonwealth for streamlined communication and collaboration across healthcare facilities.
The program utilizes a connection to Virginia’s statewide health information exchange (HIE) —ConnectVirginia — to enable health data exchange between healthcare providers, health plans, and care teams for patients receiving emergency services.
Additionally, the program integrates directly into the state’s prescription drug monitoring program (PDMP) and advance healthcare directive registry.
Enabling a connection to the state’s PDMP will equip care teams with patients’ comprehensive medication histories to promote safer prescribing practices in Virginia’s emergency departments and reduce opioid-related deaths.
“Virginia continues to be at the forefront of health care innovation, and the ED Care Coordination Program marks an important step forward in making sure Virginians in every part of the Commonwealth have access to the highest quality of care,” said Virginia Governor Ralph Northam.
“With this secure technology, we can provide emergency medical personnel with access to a patient’s critical medical information in a timely way, which will increase effective and efficient care, avoid duplicative tests, reduce unnecessary costs, and improve health outcomes,” Northam continued.
The Virginia General Assembly first established the EDCC program in 2017 within the Virginia Department of Health (VDH). The program was made possible through collabroations between health systems, health plans, physicians, VDH, the Department of Medical Assistance Services, and the Department of Health Professions.
“The ED Care Coordination Program will help ensure appropriate care in the appropriate setting for patients, while also ensuring that their personal health information is secure,” said State Health Commissioner M. Norman Oliver, MD.
The program is headed by VDH. Health IT services provider Collective Medical assists in facilitating health data exchange for ConnectVirginia to enable a connection between emergency departments.
“This program can offer peace of mind to patients and health care providers alike. I applaud the countless individuals who have worked collaboratively to make this program a reality, which helps safeguard the health and well-being of all people in Virginia and moves the state closer to becoming the healthiest state in the nation,” Oliver stated.
Looking ahead, the State Employee Health Plan and non-ERISA commercial and Medicare health plans operating within the Commonwealth intend to join the EDCC program by June 30, 2019.
The EDCC program will also expand to include other providers including primary care physicians, case managers, nursing homes, community service boards, private behavioral health providers, and Federally Qualified Health Centers (FQHCs).
These entities will have the opportunity to receive alerts and contribute patient health information to the exchange for improved care coordination.
States across the country are increasingly making efforts to improve health data access and exchange for emergency services providers for improved care coordination.
In May, the North Dakota Department of Health’s (NDHS) Division of Emergency Medical Systems launched an initiative to allow emergency medical services (EMS) personnel to engage in EHR use during patient transport.
The state health department partnered with EMS, fire department, and hospital health IT software company ESO to develop a clinical data repository designed to collect and analyze EMS patient care reports.
Enabling providers to collect and analyze patient health data helps to improve patient injury prevention efforts, performance improvement, and patient health outcomes.
“Smart data and insightful analytics can be a real game changer for organizations across the healthcare spectrum when it comes to patient transport and treatment,” said ESO Healthcare Vice President Allen Johnson.
North Dakota joins California, Colorado, Indiana, Oklahoma, New York, and other states that have taken steps to improve EHR use and health data access for emergency services organizations.
Combining the power of EHR data with machine learning may be the key to more accurately predicting mortality among cancer patients undergoing chemotherapy.
This finding comes from a recent JAMA study by Elfiky et al. that explored the effectiveness of applying machine learning to EHR data to predict patient’s short term risk of death when they start chemotherapy.
While chemotherapy significantly lowers the risk of recurrence in early-stage cancers and can improve survival rates and symptoms in later-stage disease, the treatment is challenging and costly for patients.
“These patients experience burdensome symptoms without many of the potential benefits of chemotherapy,” wrote researchers in the report.
Researchers set out to find a way to more accurately predict mortality risk before administering chemotherapy treatment to ensure patients that undergo the stress and burden of treatment will also reap its benefits.
In the cohort study, researchers analyzed the EHR data of 26,946 patients starting 51,774 chemotherapy regimens at Dana Farber/Brigham and Women’s Cancer Center from January 1, 2004 to December 31, 2014. Researchers identified the date of death for patients by linking their health records to their Social Security data.
The team classified patients by primary cancer and presence of distant-stage disease using registry data codes for metastases. With this information, researchers attempted to accurately predict death within 30 days of starting chemotherapy with a machine learning model based on single-center EHR data.
Ultimately, the machine learning model was able to accurately predict mortality rates despite lacking genetic sequencing data, cancer-specific biomarkers, or any detailed information beyond EHR data. Specifically, patient EHR data used in the machine learning model including symptoms, comorbidities, prescribed medications, and diagnostic tests.
“Mortality estimates were accurate for chemotherapy regimens with palliative and curative intent, for patients with early- and distant-stage cancer, and for patients treated with clinical trial regimens introduced in years after the model was trained,” stated researchers.
Researchers emphasized that EHR data contains “surprising amounts of signal for predicting key outcomes in patients with cancer.”
In addition to proving accurate, the machine learning model developed by researchers would also only minimally increase administrative burden on clinicians. The machine learning algorithm would not require manual data input from clinicians.
Instead, the algorithm could pull directly from existing patient EHRs.
“Although our algorithm was developed using a single institution’s data, its inputs are available nearly everywhere with an EHR,” wrote researchers.
“In addition, no special infrastructure is required to pull these data from an institution’s data warehouse; in the same way that today’s EHR systems pull a rich set of data from a database to present it to clinicians, an algorithm could pull and process the same data in real time using the processing power on a desktop computer,” the team continued.
The team also suggested the machine learning algorithm could potentially be designed to support EHR integration. Healthcare organizations could integrate the algorithm directly into existing health IT systems.
“Algorithmic predictions such as ours could be useful at several points along the care continuum,” wrote researchers. “They could provide accurate predictions of mortality risk to a clinician or foster shared decision making between the patient and clinician.”
By predicting short-term mortality for cancer patients, clinicians can identify patients who are unlikely to benefit from chemotherapy and instead may be better suited for early palliative care referral and advance care planning.
“For patients receiving systemic chemotherapy, an estimate of 30-day mortality risk may be a useful quality indicator of avoidable treatment-associated harm,” researchers concluded.
Leveraging EHR data to predict patient health outcomes may help providers to avoid clinical decisions that add unnecessary strain on patients for minimal benefit.
Improving communication between providers, diagnostic testing and medication tracking, and documentation through health IT use can help to reduce delayed, missed, and incorrect diagnoses.
This set of safe health IT use recommendations was released as part of ECRI’s Partnership for Health IT Patient Safety collaborative, which was established in 2014. The multi-stakeholder partnership is open to participation from providers, health IT companies, professional organizations, and other industry insiders.
The partnership’s most recent workgroup focused on improving patient safety during diagnostic testing, test tracking, and medication changes. Chaired by Vanderbilt University professor of pediatrics and biomedical informatics professor Christoph Lehmann, MD, the group identified three strategies healthcare organizations should keep in mind when implementing health IT solutions.
In combination, these three strategies can be effective in closing the loop of patient diagnoses.
“The goal of this Partnership workgroup was to look for technology solutions that all stakeholders could implement to close the loop — the tools provided here will help to do just that,” said ECRI Institute Program Director Lorraine Possanza.
First, the workgroup emphasized the importance of developing and applying health IT solutions that will communicate the necessary information to the correct members of a patient’s care team at the right time.
“Improve the transmission of information using standards for the formatting of normal, critical, abnormal-noncritical, and abnormal results,” wrote members of the workgroup in the report.
Efficient and effective communication between testing facilities, pharmacies, providers, and patients can enhance patient care across care settings, ECRI suggested.
“Designing, testing, deploying, and implementing health IT solutions to improve these communication pathways has the potential to make closing the loop a seamless and elegant process, with all diagnostic results and medications communicated to the provider, the pharmacy, and the patient,” wrote the workgroup.
ECRI also emphasized the importance of tracking diagnostic results and medication changes.
“Tracking of diagnostic results and medication changes is a time-consuming, burdensome task, but necessary to ensure a closed loop,” noted report authors. “Identification of interruptions and potential failure points in the process is critical to find and react to failures to close the loop.”
The workgroup recommended healthcare organizations identify where health IT can be useful for resolving deficiencies and improving medication and test result tracking.
Using EHR functionality to track diagnostic testing and medication changes may also be helpful. Furthermore, implementing lab standards — such as LOINC — may help to automate accurate matching of results and ordered tests to close loops.
Finally, ECRI suggested healthcare organizations use health IT to link, acknowledge, and document the review of information and action taken.
“This step includes the actor reviewing and acknowledging or acting upon information,” clarified report authors.
Toward this end, the workgroup recommended healthcare organizations help to improve interoperability by integrating EHR systems and other health IT modules across the care continuum to facilitate communication and documentation across care settings.
“This is to facilitate communication and acknowledgment, including the use of application programming interfaces (APIs) to allow laboratory systems and hospitals to communicate, as well as the use of HL7 and fast healthcare interoperability resources (FHIR) to aggregate and merge patient data from separate data sources,” wrote the workgroup.
Developing EHR functionality such as diagnostic results notifications may help to promote communication, acknowledgement, and documentation of diagnostic testing, medication changes, and actions taken.
Implementing these health IT use practices and closing the loop will help to avoid delayed or missed diagnoses, which may lead to patient harm.
“These safe practice recommendations are a call to action,” maintained the workgroup. “Although the EHR and its technology components have the potential to facilitate timely follow-up across all healthcare settings, it may take regulatory efforts to make this possible.”