The growth of health information technology brings into play many new concepts. Here, we sort out some of the key points, supporting concepts and give reporters an easy way to explain such things to their readers, viewers and listeners.
APIs are important in health IT because they allow programmers to access key data from other sources and integrate that data into their own applications. Think of an API as a conduit that allows program creators to “grab“ data sources.
A good example of consumer API use is a smartphone weather app, which takes data from other sources and organizes it so you know whether you'll need sunscreen or a snow shovel.
Allscripts was a pioneer in open APIs through its AllScripts Developer Program, which allows programmers to access the API, documentation, code samples and validation resources to integrate with Allscripts platforms. The thinking behind this approach is that open APIs result in a better, richer end-user experience.
APIs have the potential to improve interoperability and patient access to their own data. APIs can also help create more useful resources on patient care.
The National Cancer Institute announced in September 2016 that it had released the beta of an API to make the information on clinical trials (trials.cancer.gov) available to third parties. The API will allow advocacy and patient groups, cancer centers and researchers to build apps and tools to help patients find potentially suitable clinical trials. You can read more about that announcement, made by Vice President Joe Biden in June, and initial uses of the API.
The Office of the National Coordinator on Health IT (ONC) has an overview on APIs, especially in relation to electronic health records, in its Health IT Playbook. In 2015, the ONC included API criteria in its EHR certification requirements.
Fast Healthcare Interoperability Resources, FHIR (pronounced “fire“), is a standardized API being created by Health Level Seven, and expected for release in 2017. The ONC is supporting the development of FHIR-based API.
Clinical decision support (CDS) system
A clinical decision support (CDS) system is a computer application that is used to analyze available data to help providers make clinical decisions, including diagnoses and treatment plans. The goal of CDS is to improve patient safety and quality and, therefore, outcomes.
CDS systems combine patient information with a medical knowledge database to offer decision support. Nurses, physicians and other providers typically use these systems.
There is not a one-size-fits-all approach to CDS systems. They can include order sets, drug interactions, care plans and protocols, critiques, alerts and other warnings, predictive analytics and relevant data summaries for patients.
While CDS can help bolster evidence-based practices, they are a work in progress and face challenges including fitting into provider workflows, addressing co-morbidities of patients and create recommendations that are tailored to individual patients and their circumstances.
Importantly, the Centers for Medicare and Medicaid services is proposing phasing out clinical decision support system requirements from its physician and hospital EHR incentive programs. (link to MACRA tipsheet)
Cognitive computing is the simulation of human thought process in a computerized model. Cognitive computing is used in artificial intelligence (AI) applications such as robotics and virtual reality. It essentially harnesses big data, cloud computing, pattern recognition and natural language processing to mimic how the human brain processes information.
Cognitive computing in health care is expected to take off in the next few years. One example of its application to healthcare is IBM Watson for Oncology, which was created with Memorial Sloan Kettering Cancer Center in New York to help cancer specialists make more informed treatment decisions. IBM Watson for Oncology analyzes a patient's personal medical data against huge data troves and expertise to offer evidence-based treatment options to individual patients.
Some see promise in cognitive computing as a way to solve entrenched problems in health care, from health disparities to physician burnout.
Electronic health record (EHR) adoption
Adoption of electronic health records (EHRs) by hospitals, physician groups and sole practitioners has risen steadily in recent years. At least 75 percent of hospitals have now adopted at least a basic EHR, up from 59 percent in 2013, according to a Dec. 2015 report in Health Affairs. And 56 percent of all U.S. office-based physicians have demonstrated “meaningful use“ of certified EHRs at the end of 2015, according to the HHS.
The rapid adoption of EHRs is the result of the HITECH Act of 2009. The HITECH – Health Information Technology for Economic and Clinical Health – Act established a financial incentive program for providers to adopt, implement and upgrade certified EHR systems. Called the “meaningful use“ (MU) program, it started in 2011. Between May 2011 and March 2016, a total of $22.6 billion in payments have been made to providers. Medicaid providers have received another $10.5 billion in that same time period. As of October 2015, more than 479,000 health providers have received incentive payments through the program.
Meaningful use is in three stages: Stage 1 is the basic capture and sharing of data; Stage 2 is advanced clinical processes; and Stage 3 focuses on improved outcomes. Most participants had to attest to Stage 2 by 2015. Stage 3 attestation is in 2016.
Starting in 2015, hospitals participating in the Medicare portion of the EHR incentive program faced financial penalties for not meeting requirements.
While the rapid adoption of EHRs has been applauded, some issues persist. These include:
Financial costs: Providers have reported challenges related to both upfront and ongoing costs of EHRs (including upgrades).
Physician cooperation: Already very busy physicians have to input patient data into records, and some experts have been talking about this issue and how to ease this administrative burden.
Complexities of meeting meaningful use criteria: Having the IT personnel and funding to meet the criteria have been issues for some providers.
Digital divide between providers: In some studies, smaller and rural hospitals and practitioners were less likely to be able to meet meaningful use criteria.
Interoperability: The ability to exchange patient data between providers and with public health entities is an ongoing issue. A 2015 General Accounting Office report identified five barriers to interoperability.
Health Information Exchange Organization
A Health Information Exchange Organization (HIEs or HIOs) is an entity that provides health information exchange services to participating stakeholders in one geographical region. These organizations are also known as RHIOs (pronounced “RIOs“ but that term is generally no longer used). HIEs/HIOs typically do the legwork in terms of meeting capability, security and privacy standards for secure exchange of health information among participants. Stakeholders often include providers, laboratories, payers and public health departments in the region. These organizations can be regional or statewide. They must comply with HIPAA and other privacy laws. And they often provide technical and advisory support services to participants as well.
A list of HIEs/HIOs that have received federal funding can be found on the HIE tipsheet.
Medication reconciliation, or med rec, as it is commonly referred to by clinicians, is not a health IT topic per se, but technology is increasingly being used to improve the medication reconciliation process.
Medication reconciliation is the process of eliminating discrepancies in patient pharmacy data and electronic medical records. These discrepancies typically arise during transitions of care. So when a patient is discharged from the hospital with new medications, older drugs may not be taken off a patient's list of prescribed medications. Similarly, inpatient clinicians might not be able to access pre-admission medication lists.
More than half of all patients had at least one medication discrepancy at time of hospital admission, according to one study.
Medication discrepancies can result in incorrectly documented dosages, duplication of medications prescribed or omitted medications in the patient's electronic medical record. A lack of medication reconciliation can result in drug interactions or failure to take medications as prescribed, which can cause preventable readmissions.
Many hospitals are working to improve care transitions between the hospital and outpatient care environments. Medication reconciliation is an important aspect of care transition improvement efforts. The med rec team typically includes pharmacists and nurses, who review medication lists with patients either at the bedside prior to discharge from the hospital or over the phone after the patient goes home.
Technology is playing an increasingly important role in medication reconciliation. Integrating EHRs with outpatient pharmacy records is one example. And computerized provider order entry (CPOE) is improving accuracy of medication orders. However, researchers note that fully implemented EHRs in hospitals and outpatient centers have not eliminated the problem of errors in patients' medication lists.
In patient matching, the right data is matched with the right patient at the right time, in a secure and private structure.
The push for better patient matching originated from the idea that accurate patient identification can reduce the risk of medical errors and improve care quality and safety. Patient matching can also reduce inefficiencies in care, such as unnecessary tests.
If you think about the sheer number of John Smiths or Sarah Browns or David Lees in existence, name matching for correct medical information is both important and challenging.
In 2014, a report by the Office of the National Coordinator for Health Information Technology (ONC) found that about seven out of 100 patient records are mismatched. Patient matching error rates rise as patient information is shared with other health entities, such as between a hospital and a nursing home.
Population health management is the aggregation of patient data across multiple health IT resources, analyzing that data, and using those data to improve outcomes and better track the health of communities and specific populations. Population health management is already mainstream, largely because of the implementation of EHRs in recent years and using those EHRs to create actionable databases and disease registries, which group patients by disease states including diabetes, hypertension, HIV/AIDS and depression.
Providers often target patients in disease registries for more supportive interventions. Population health management can also help providers identify high-risk and frequent users of hospital resources, like emergency departments. And population health management can track rates and test interventions for community public health concerns including annual flu vaccines and tobacco cessation. The federal government is encouraging population health management through its Shared Savings Program, state Medicaid waivers and the Medicare Advantage program. For instance, public hospitals in states that have received a Medicaid waiver often have financial incentives to implement population health management capabilities.
Population health management holds the potential to reduce health care costs by moving interventions “upstream“ to reach patients in more individualized ways before they end up seeking care in more acute (and costly) settings. Population health management operates in concert with patient-centered medical homes, where primary care providers use a team-based approach to improve care coordination, patient engagement and quality and safety.
Regional Health Information Organization
A Regional Health Information Organization (RHIO) (pronounced “Rio“) is an entity that provides health information exchange services to participating stakeholders in a geographical region. RHIOs typically do the legwork in terms of meeting capability, security and privacy standards for secure exchange of health information among participants. Stakeholders often include providers, laboratories, payers and public health departments in the region. RHIOs must comply with HIPAA and other privacy laws. RHIOs often provide technical and advisory support services to participants as well.
Remote patient monitoring is the use of technology to monitor the health of patients outside of conventional clinical settings. This type of monitoring most often happens at home, but can also be used in long-term care facilities, for instance.
Monitoring programs collect data on select patients and then transmit that data to care providers in another location for assessment, recommendations and response. Collected data might be vital signs, weight, blood pressure, blood sugar, heart rate or electrocardiograms. Remote patient monitoring is gaining traction as the technology to track patients improves and as hospitals and other providers dedicate nurses and other clinicians to conduct the monitoring. The idea is that remote patient monitoring can keep people in their homes, reduce ER visits and avoidable readmissions and improve patient satisfaction and outcomes.
Medicare penalizes hospitals for excessive 30-day readmissions. Patients typically receive devices and software programs that aim to be easy to use to monitor their health at home. These systems are typically integrated with patient EHRs and are combined with high-touch care such as frequent phone calls from nurses or health coaches, and possibly home health visits. Remote patient monitoring is a form of telehealth.
Some roadblocks to remote patient monitoring include: payment, especially since most of this care is not currently reimbursable in a fee-for-service environment; staffing needs, such as making sure that staffing is adequate to capture and respond to incoming data from at-home patients; fitting into workflow, specifically making sure incoming data is actionable and works with physician time; and legal gray areas and liability around caring for patients in non-traditional settings.
Unique Device Identification (UDI)
What is a UDI?
Is it easier to track a jar of peanut butter than a medical device? Some say yes, and so a large-scale effort is underway to make it easier to identify medical devices in their distribution and use.
Once in place, this tracking system will not only allow for government regulators, device makers, hospitals and surgery centers to better monitor devices, it could also allow for deeper research into the comparative effectiveness of devices.
The basis of this system is a UDI – a unique numeric and alphanumeric code that is made up of two parts: 1) a device identifier that includes the labeler (e.g. manufacturer) and version or model of the device; and 2) an identifier that shows the serial number, expiration date, manufactured date, batch number or other unique codes specific to the item.
UDI monitoring system getting underway
In 2013, the Food and Drug Administration issued a final rule establishing the UDI system that is now being rolled out in phases. UDIs must appear in plain text and also in a digital form called automatic identification and data capture (AIDC) so it can be entered into an electronic medical record or other computer system in an automated (opposed to paper-based) process.
An important aspect of this UDI system is the FDA-administered Global Unique Device Identification Database (GUDID), where information about each device will be housed. This information is available to the public at Access GUDID. Users of this database can search on specific devices and also download information on every device entered into the database. The FDA says it updates the database daily.
Timeline for implementation
The UDI system is going into effect in stages over seven years but it has delayed some initial deadlines. Medical devices that are implantable and life-saving have taken priority. But many other devices of lower classes will also need UDIs, like wheelchairs, pregnancy tests and other durable medical goods. The FDA has a chart on deadlines. It should be noted that many device manufactures have said they are not prepared to meet these deadlines. One survey in August 2016 indicated that only 15% of device manufacturers were prepared to meet an upcoming deadline.
To understand what unstructured data is, let's first clarify what structured data is: information that can be sorted into tables and rows and that is readily available, such as billing and diagnosis data. An example of structured data is the fields that clinicians fill in to a patient's electronic health record (EHR).
By contrast, unstructured data is information that is not easily organized and often in disperse locations. An example of unstructured data is physician notes in the EHR.
Figuring out how to organize and analyze unstructured data is one puzzle many companies are attempting to solve in health care today.
It's an important puzzle because of the sheer volume of unstructured data – collected via wearables, remote monitoring systems, social media, scales, sensors, patient reports and images such as X-rays. Linking unstructured data to structured data could provide a more complete picture of the health of an individual or a population. For instance, let's say a hospital system is attempting to find out which of its patients are smokers to target them for smoking cessation interventions. That information, “smoker,“ might be documented only as a note written by the physician in the EHR – as unstructured data. Extracting "smoker" from the note might not be easy. Sometimes it requires a manual chart review. The ability to take relevant information from a variety of sources and then merge those sources into a central place for analysis could help improve care and provide better public health overall.