[Editor’s note: Updated October 30, 2015]
Each industry has its own type of information technology and a particular group of terms that goes along with it. Healthcare is no exception in this regard. In fact, healthcare has even more industry-specific terms than most other business sectors do. That’s partly because of its complexity and also because it’s going through a radical change in how products are delivered and paid for.
Healthcare has a large number of moving parts that include many kinds of care providers, each of which has its own type of information systems. The systems that a hospital uses are different from those of a physician practice, and both kinds of systems differ greatly from the IT used by pharmacies, home health agencies and nursing homes. Moreover, a large medical center may have dozens or even hundreds of different systems across its departments and medical equipment.
Healthcare is also very fragmented. While there are huge healthcare systems that encompass everything from soup to nuts (think Kaiser Permanente), there are myriad healthcare providers of every stripe in the country’s metropolitan areas. A large number of IT vendors supply this giant market. Just in ambulatory care – care provided outside of the hospital – there are hundreds of electronic health records (EHR) vendors, although about a dozen of them divide most of the market.
While healthcare has a reputation for being resistant to IT, physicians and nurses are not technophobes. Even before EHRs came along, they welcomed medical advances that depended heavily on new technology. A small but enthusiastic cadre of physicians pioneered the early EHRs. But healthcare providers want IT to support them in providing better care without increasing their workload. Unfortunately, the systems that have been developed up to now require them to spend more time on documentation than they used to, reducing their productivity. EHRs also change their workflow. So even though there is some evidence that health IT has improved the quality of care, many physicians are frustrated and unhappy with their EHRs.
This brief overview of the health IT landscape only begins to convey the breadth and complexity of the field. The following glossary provides more information on what health IT is all about.
Accountable care organizations (ACOs)
The centerpiece of the Obama Administration’s efforts to control healthcare costs, ACOs are groups of hospitals and physicians or just physicians that are committed to improving the quality and lowering the cost of care. Some ACOs have contracted with the Medicare Shared Savings Program, the Medicare Pioneer Program, or state Medicaid programs; others have commercial insurance contracts; and some have both government and private-sector business. There are about 600 ACOs, of which the majority are physician-led. Most ACOs today split the savings they achieve with their payers. But some are starting to take downside financial risk as well, and more are expected to follow suit in coming years.
Activity-based cost accounting
A new type of application known as activity-based cost accounting software gives healthcare organizations the ability to calculate the cost of each episode of care. While few hospitals and healthcare systems use this approach today, more are expected to adopt it because of the growth of value-based reimbursement and payment bundling. Instead of drawing inferences from billing or claims data, the new software allows organizations to analyze every cost element in the episode, including hospital, physician, medication, and ancillary expenses. This is going to be important for hospitals going forward as more of them accept bundled payments and global payments, both of which involve multiple providers of care.
Admission-transfer-discharge (ADT) systems
ADT systems, the core of hospital financial systems, track the admissions, transfers, and discharges of patients. They allow hospitals to know how many patients they have at any particular time and how long they have been in the hospital. Not only is this information important for operational purposes, but it enables management to calculate the average length of stay – a crucial metric for determining the rate of bed turnover. In the last year or two, some accountable care organizations (ACOs) and HIEs have been given access to hospital ADT systems. This enables them to tell primary care doctors when their patients have been discharged so that they can pick up their care right away.
Ambulatory care EHRs
EHRs in physician practices run the gamut from fairly simple systems designed for small offices to very sophisticated EHRs used mainly by large groups, many of them owned by hospitals. There are hundreds of these systems, but a dozen of them control 75 percent of the market. Government certification rules for EHRs have narrowed the field, forcing vendors with weak products to drop out. Ambulatory care EHRs typically integrate the clinical modules with billing and scheduling systems, making it easy to send billing codes to the financial side. Many ambulatory care EHRs are cloud-based, especially those that target small practices.
A mobile data aggregation platform, HealthKit is interfaced with some widely used EHR systems. Patients can send their mobile data to HealthKit, view it there, and authorize sharing of the data with their providers. Healthcare organizations have begun to use HealthKit in different ways. For example, Cedars-Sinai is asking patients to tell their physicians what they’re interested in using the data for. Duke Medicine, in contrast, is focusing on specific conditions, starting with heart failure. Duke has connected HealthKit with the mobile app for its patient portal. When a doctor wants to monitor a patient, he or she sends that patient a message saying that HealthKit is available. The patient can then authorize access to the data on his or her smartphone, and the physician pulls it into the EHR.
ResearchKit is an open-source framework that allows developers to create smartphone apps for medical research. The initial apps enabled researchers to gather data from smartphone users participating in studies of diabetes, asthma, Parkinson’s disease, cardiovascular disease, and breast cancer. By the fall of 2015, 50 researchers had developed ResearchKit apps available in the Apple store. More than 100,000 people have enrolled in ResearchKit studies being conducted in such academic centers as Duke University, Johns Hopkins University, Oregon Health and Sciences University, the University of Southern California, and Boston Children’s Hospital. ResearchKit is integrated with HealthKit, allowing researchers to use data collected from a variety of app-enabled monitoring devices.
Automation tools are designed to prompt or execute actions that can contribute to better health. For example, most EHRs have “health maintenance alerts” that pop up in electronic charts when providers see patients, and standalone registries generate more comprehensive alerts. Some registries are combined with clinical protocols to generate automated messaging to patients who are overdue for office visits or tests. Registries can also be used in automated online campaigns to educate people who have specific kinds of chronic diseases. And PHM software populates dashboards that care managers can use to determine which patients most need their help on a daily basis.
Big data, which has made an impact everywhere as computers have grown faster and more powerful, is becoming a major factor in healthcare. The three main characteristics of big data – volume, velocity and variety – increasingly challenge healthcare organizations as EHRs, financial systems,and imaging systems spew out ever more data. Population health management requires the capability to risk-stratify populations, track and intervene with patients, and predict which patients will get sicker or sicker. To do that, organizations must aggregate, normalize and analyze a wide variety of data across many care settings. The advent of genomic sequencing and personalized medicine is starting to add a new level of complexity and giant data sets that only big data techniques are capable of addressing.
Business & clinical intelligence
Business intelligence (BI) applications address financial and operational aspects of healthcare systems, such as contract negotiations, facility management, measurement of resource utilization, and cost analysis. Clinical intelligence (CI) software supports activities such as quality improvement, care management, and population health management. BI and CI overlap in a number of areas, such as an organization’s staffing needs. Both are needed to evaluate the efficiency and quality of care provided by an organization or an individual provider. Measures of efficiency include average length of stay and readmission rates, both of which are affected by the quality of care.
BYOD (Bring Your Own Device)
Since the advent of smartphones and tablets, more and more clinicians have been bringing them to work. BYOD has created concern among hospital administrators because of its security risks. Some healthcare systems allow doctors and nurses to use only hospital-supplied mobile devices when they are on the hospital campus. Others allow people to bring their own devices but prohibit them from storing protected health information on them. Nevertheless, clinicians have discovered that the easiest way to get one other’s attention quickly is by texting their colleagues. To address that trend, a growing number of hospitals have adopted secure texting systems.
Capitation is a payment method under which physicians, physician practices, or other healthcare organizations receive a flat rate for all services they provide to a particular person during a period of time. In health plan contracts, the amount is usually stated in dollars per member per month (PMPM). The provider gets the monthly capitation payment regardless of whether the patient seeks or receives care. This population-based approach places the provider at financial risk. If the value of the care delivered to members of a patient panel exceeds the total capitation amount for that panel, the provider loses money; if it falls below the budget, the provider keeps the difference. In a variant known as “global capitation,” a provider is financially responsible for all care that the patient receives, including hospitalization and the cost of drugs.
Electronic claims clearinghouses are a vital link in the chain that connects healthcare providers to payers. While some providers directly bill their larger payers, such as Medicare and Blue Cross/Blue Shield plans, most claims go through claims clearinghouses to the multitude of health plans and government agencies that provide health insurance. Clearinghouses edit the claims so that they can be processed by the many different systems of private insurers and government intermediaries. In some cases, clearinghouses bounce claims back to providers if they’re missing information or were submitted in the wrong format. In addition, the clearinghouses submit and return responses to provider inquiries such as eligibility and claims status requests. They also route electronic remittance advance that providers need for payment posting and claims denial management.
Clinical decision support (CDS)
Clinical decision support consists of reminders and alerts that prompt health IT users either to do something they might otherwise forget or to avoid an action that might harm a patient. In the second category are drug interaction checkers included in the electronic prescribing modules of EHRs and standalone e-prescribing applications. These systems warn physicians when a drug they’re prescribing might have an adverse interaction with another medication the patient is taking or with a known allergy of the patient. Many EHRs also have health maintenance alerts that remind physicians to provide preventive or chronic care services that are recommended for patients with particular characteristics. These are not as robust as standalone electronic registries, which contain more specific and timely information about patients. Meaningful use requires use of CDS.
Clinical documentation improvement (CDI)
CDI is an approach, often supported by specialized software, designed to help physicians improve their documentation of patient encounters and procedures. Implemented chiefly by hospitals, CDI programs aim to present a more accurate description of the care that has been delivered to patients. Better documentation can contribute to better continuity of care, because it helps subsequent caregivers understand a patient’s status and what has been done for him or her. In addition, it can boost a hospital’s bottom line by ensuring that the severity of a patient’s condition is properly documented to support the maximum allowable charges. And under the ICD-10 diagnostic coding system, which is much more complex than the previous coding system (see separate entry), complete documentation is essential to support insurance claims.
In recent years, the quantum leap in the speed and bandwidth of Internet connections has made it practical to base EHRs in the cloud. Many physician practices, especially smaller ones, have taken advantage of this option. These groups would rather pay a monthly fee that covers maintenance than make a large upfront investment in servers and software – even though the five-year cost of ownership is roughly similar in either case. Hospitals, in contrast, have been slow to move to the cloud. In 2011, just 55 percent of them had any data or applications in the cloud. By 2014, 83 percent of hospitals did, but only half of them had any cloud-based clinical applications.
A branch of artificial intelligence, cognitive computing uses machine learning and massively parallel computer processing to build on big data techniques. As typified by IBM Watson, the supercomputer that won the “Jeopardy” game in 2011, a cognitive computing system is a collection of overlapping, reasoning algorithms that can be expanded and updated as the system learns from experience. The applications of cognitive computing in healthcare include advanced natural language processing, the ability to convert unstructured data into structured data, the ability to search the medical literature quickly for clinical decision support, and the ability to correlate large numbers of unrelated data sources, including genomics and non-medical determinants of health, to find clinically useful connections.
Computer assisted coding (CAC)
Another new type of application uses natural language processing to help hospital coders pick the correct codes for a given office visit, test or procedure. CAC does this by extracting code-related terms from electronic text to supplement the coded elements in the EHR’s structured fields. It has been shown to improve productivity by automating parts of the coding process. In outpatient departments such as radiology and pathology, CAC can automate most of the coding, but more human intervention is required in inpatient coding. CAC is expected to help health care organizations cope with the new requirements of ICD-10 coding (see the regulatory section).
Computerized practitioner order entry (CPOE)
CPOE is the process of entering electronic orders for medications and tests with the help of computerized clinical decision support. Used in acute-care hospitals and emergency departments, CPOE has proved to be challenging for staff physicians, some of whom are not computer-literate and have had no experience entering orders into a computer. As a result, doctors sometimes delegate these tasks to nurses. By doing so, they lose the benefit of the evidence-based decision support, which can help them avoid adverse drug interactions and redundant testing. CPOE can be difficult to use, however, and, in some cases, has been blamed for endangering patient safety.
In place of traditional data warehouses, some healthcare organizations use a big data approach known as a “data lake.” Instead of relying on a relational database, a data lake often employs the Hadoop software framework for distributed storage and distributed processing of large datasets in cloud-based computer clusters. Massively parallel computing and a late-binding rules approach allow a wide variety of data to be aggregated quickly. The data interface does not have to be rewritten to accommodate new kinds of queries or use cases. Reports can be rapidly assembled by using configuration files that identify business rules at run-time. This ad hoc approach enables reports to be delivered in as little as a day — a feature that can be valuable in both patient and population health management.
Healthcare systems and accountable care organizations (ACOs) use data warehouses to aggregate, normalize and analyze data from multiple systems. Before healthcare organizations began using business and clinical intelligence tools, few of them had data warehouses. Far more of them do today, but most of these organizations are still doing retrospective analyses that allow some latency in the database. As predictive modeling to forecast the health risks of individuals and populations takes center stage, some organizations are adopting a “late-binding” data warehouse architecture. This approach allows them to assemble data quickly for particular purposes by binding data to business rules on an as-needed basis rather than programming it all beforehand.
The Direct Project, a secure clinical messaging protocol based on standard Internet protocols, was devised in 2011 by a private/public consortium. Direct messaging allows providers to push messages with document attachments to other providers. Health information service providers (HISPs), most of them owned or contracted by EHR vendors, handle the transmission of these messages and make sure they get to the right providers at their Direct addresses. Under the 2014 EHR certification rules, vendors are required to include Direct capability in their products so that providers can exchange care summaries. Despite all of these efforts, only a small minority of providers used Direct messaging in the first half of 2015. But a recent survey shows that two-thirds of HIEs are using the protocol for enabling data exchange among their participants.
To qualify for meaningful use incentives, eligible providers must use certified EHRs that have been tested by government-approved certification bodies. The certification criteria have been devised so that users of these EHRs have all of the capabilities needed to show meaningful use. So, like the EHR incentive program, the certification program has grown more complicated and demanding over time. Because many EHR vendors had difficulty in rewriting their applications, CMS allowed providers to use EHRs certified under 2011 rules in 2014, but all of them had to use 2014-edition EHRs in 2015 to qualify for meaningful use. By 2018, everyone will have to use EHRs that have been certified to new standards that will enable them to meet the meaningful use stage 3 requirements.
Government regulations promise onerous fines and public scrutiny to healthcare organizations that allow data security breaches. So hospital systems, in particular, are security-conscious and have gone to great lengths to protect the security of protected health information (PHI). For example, many healthcare organizations don’t allow clinicians to store PHI on end-user devices. They keep everything on the server and, in some cases, adopt a virtual desktop approach to facilitate the use of EHRs and other applications. Nevertheless, security breaches are increasing at a frightening rate, partly because of the theft or loss of unencrypted laptops and other mobile devices. Meanwhile, many physician practices have yet to perform government-mandated security risk assessments.
eICU and telestroke
Both of these terms refer to the use of remote monitoring technology to provide appropriate staffing to understaffed areas of community hospitals. In an eICU setup, a critical care specialist in an academic medical center or a remote monitoring center can track patients in another hospital’s intensive care unit, using both monitoring data and remote cameras. Some organizations have used this strategy to reduce costs and improve patient outcomes. Telestroke systems allow neurologists to diagnose strokes remotely in time to order life-saving “clot buster” medications. The neurologists review and interpret CT brain scans that have been stored and forwarded, and they examine the patient via videoconferencing. In some systems, robots with cameras wheel up to the patient’s bed, allowing the examining physician to make more accurate observations.
Electronic health records (EHRs)
Also known as electronic medical records (EMRs), EHRs have been commercial products since the mid-1990s, but didn’t begin to catch on among physicians until about 10 years ago. Besides replacing paper charts with electronic documentation, ambulatory care EHRs include diagnosis, allergy and drug lists, modules for ordering tests and medications, care plans and clinical decision support features. Hospitals also have EHRs for their inpatient and outpatient departments, including emergency departments. Larger healthcare organizations connect their hospital systems with the ambulatory care EHRs used in physician offices. In some cases, they do this through interfaces; in other cases, everyone uses products from the same EHR vendor. Different kinds of EHRs are found in nursing homes, rehab facilities and home health agencies.
Electronic payment posting and funds transfer
Electronic payment posting is a feature of most practice management/hospital financial systems. When electronic remittance advice (ERA) comes into the system from a health plan, it can automatically post a payment to the account. This is a great time saver and is much more accurate than manual posting. Denial management staff can also use the ERA to pinpoint problems in denied claims so they can correct and resubmit them. Many insurers also transfer payments automatically to providers’ bank accounts, speeding up their cash flow. For this system to work properly, payment posting and ETF must be in synch with each other.
Once confined to standalone e-prescribers, electronic prescribing is now a core function of most EHRs. According to Surescripts, which processed 1.2 billion e-prescriptions in 2014, 56 percent of physicians prescribed electronically that year. E-prescribers include drug interaction checkers and other types of clinical decision support. They record the prescriptions automatically in the EHR, and the EHR’s scheduling system populates the demographic fields (name, date of birth, insurance plan, etc.) in each e-prescription. Until recently, most states prohibited electronic prescribing of controlled substances; that has now changed, but few e-prescribers allow doctors to prescribe controlled substances.
Fast Healthcare Interoperability Resources (FHIR)
FHIR is a new standards framework from Health Level Seven (HL7), the leading healthcare standards development organization. In conjunction with Restful APIs, the Oauth authorization standard, and a visualization layer called SMART, FHIR promises to facilitate interoperability, broaden EHR capabilities, and accelerate innovation in the use of mobile health apps. FHIR uses snippets of data known as resources to represent clinical entities within EHRs in a web services context. Non-proprietary APIs can be used to connect FHIR applications to any FHIR-enabled EHR without customized interfaces. A coalition of 40-plus EHR suppliers and other stakeholders is currently building out and testing FHIR. Eventually, experts say, FHIR should enable providers to exchange discrete data directly between EHRs, using cloud-based networks.
FDA mHealth regulations
The Food & Drug Administration (FDA) regulates mobile health apps as medical devices, but only if they fall into one of three categories. First, FDA approval is required to market an app that functions like a device that the FDA already regulates, such as an app that turns an iPhone into an electrocardiography (ECG) machine. Second, the FDA regulates apps that are accessories to a regulated device, such as a tablet app that displays x-rays from an FDA-approved PACS. Third, mobile medical apps that suggest diagnoses and provide treatment advice are regulated. Taken together, these regulated products form a very small portion of the estimated 63,000 mHealth apps on the market.
Following the publication of the first human genome sequence in 2003, researchers began using big data methods to cope with the huge amounts of data generated by sequencing. As sequencing costs have dropped astronomically and as the healthcare industry has adopted EHRs, the pace of research has accelerated. Researchers are now racing to match the variants in the genotypes of patients with the differences in their phenotypes —their individual characteristics and diseases – to figure out which genes may be responsible for particular health conditions. As this work progresses, the possibility of personalized medicine is starting to be actualized, especially in cancer care. But much more work needs to be done to help physicians interpret genetic signs and correlate them with symptoms.
Health information exchange (HIE)
This term refers both to the act of exchanging health data and an organization that facilitates information exchange. HIEs may be statewide, regional, metropolitan, or organization-specific. The latter, known as private HIEs, have been growing more quickly than public HIEs in recent years. That is partly because public HIEs have had difficulty in providing a return on investment to local providers. Indeed, most HIEs subsisted on government grants until they dried up, and the majority of them have yet to find a viable business model. Some HIEs offer data analysis services and help in meeting the “transitions of care” requirements of the government’s EHR incentive program (see the section on government regulations).
Health information service providers (HISPs)
HISPs are the glue that holds together direct secure messaging. HISPs transport messages from one provider to another and supply directories of Direct addresses so that physicians or hospitals can find one another on the Direct network. HISPs may be independent or owned by EHR vendors; they can also be regional or national. The big problem that HISPs had to address early on was the lack of trust among them. Without knowing who they were dealing with, they didn’t want to convey messages that included protected health information (PHI). DirectTrust, a nonprofit trade organization, has done a lot to overcome this lack of trust by accrediting the leading HISPs through the Electronic Healthcare Network Accreditation Commission (EHNAC). But the HISPs have still not agreed upon a standardized method to share their provider directories.
Health Level 7
Founded in 1987, this nonprofit, member-governed body has developed IT standards used in the global healthcare industry. Virtually all large U.S. healthcare organizations use Health Level 7 (HL7) messaging to connect their systems, often through some kind of middleware. However, HL7-based interfaces are insufficient to provide interoperability between disparate EHRs. In addition, HL7 permits customization that, over time, has produced significant differences in how its standards are applied in various organizations. HL7 developed a Clinical Data Architecture (CDA) used in clinical summaries that providers must exchange under the meaningful use program. And as mentioned above, the HL7 draft standards framework known as Fast Interoperability Health Resources (FHIR) has raised expectations that it will increase interoperability.
The acronym HIPAA refers to the federal Health Insurance Portability and Accountability Act of 1996. The original intent of the law was to help people keep health insurance when they switched or lost jobs. HIPAA also requires providers to protect the privacy and security of health information and to take steps to control administrative costs by simplifying electronic transactions. CMS has implemented a number of measures to standardize the electronic exchange of administrative data, including claims, eligibility, claims status, ERA, and EFT. But the most important part of HIPAA for healthcare providers and consumers have been the privacy and security provisions, which were strengthened by the same 2009 law that created the meaningful use program. Penalties for violations of these provisions were increased to up to $1.5 million per violation, depending on the circumstances. So healthcare providers – who already were very wary about violations of patient confidentiality – have stepped up their efforts to prevent data security breaches. As mentioned earlier, however, the number of breaches continues to grow.
Hospital information systems
Hospital EHRs include many components that EHRs for office-based doctors lack, including ancillary clinical systems, electronic medication administration records, and computerized practitioner order entry (CPOE). In addition, they have both nursing and physician documentation. Hospital information systems are very complex and include products developed by vendors other than the healthcare system’s main EHR vendor, such as lab, pharmacy, and radiology picture archiving and communications systems (PACS). To help these systems exchange information, hospitals may use interfaces based on Health Level 7 (HL7) standards, middleware, or enterprise viewers for disparate PACS.
Since Oct. 1, 2015, healthcare providers have had to use the International Classification of Diseases (ICD)-10 diagnostic code set in order to file claims with Medicare, Medicaid, and private payers. This has been a monumental shift for the industry, since the current ICD-9 code set has about a fifth as many codes as ICD-10 does. Physicians and billers had to be trained to select the correct codes, and healthcare organizations had to do extensive internal and external testing. CMS struck an agreement with the American Medical Association (AMA) to allow Medicare claims to be paid for the first year if coders got the primary ICD-10 codes right. But commercial payers did not follow suit. While the initial transition has been fairly smooth, it’s still unclear how much disruption there will be in payments.
The meaningful use and EHR certification rules include a number of provisions related to interoperability, which refers to the ability of different health IT systems to communicate with one another. At one level, this can mean the exchange of secure messages with document attachments. But for the kind of data liquidity that analytics require, EHRs should be able to ingest data from other systems and sort it into the appropriate fields, with provider approval. Up to now, interoperability at either of these levels has been very limited. The government has been reluctant to prescribe standards to the private sector, and the healthcare industry’s efforts to promote interoperability have run into complex business and technical barriers. However, some new approaches such as Direct messaging and FHIR are promising, and some of the leading EHR vendors and HIEs have banded together in various coalitions to pave the way for interoperability.
The government EHR incentive program, which began in 2011, requires eligible hospitals and eligible professionals to show “meaningful use” of their EHRs to qualify for the government funds. The meaningful use criteria get more difficult during the three phases of the program. In stage 2, the current phase, eligible providers must use their EHR for prescription and lab orders, record vital signs, maintain diagnosis and medication lists, provide a portion of their patients with online visit summaries, have at least 5 percent of patients view, download or transmit their electronic records (this rule has been scaled back), exchange clinical summaries with other providers in a percentage of “transitions of care” (such as hospital discharges and referrals to specialists), use clinical decision support tools, incorporate lab results into their EHRs, report on clinical quality measures, and provide reminders to patients for preventive and follow-up care. To date, CMS has spent more than $30 billion on EHR incentives. Providers have received the bulk of their incentives and now face financial penalties for not showing meaningful use.
Medicare fraud and abuse audits
CMS contractors do random audits of physicians, hospitals and other providers to find out whether they are defrauding Medicare, usually by sending by charging more than they should or by charging for services they didn’t perform. (Medicare sets fee schedules every year, but providers can “upcode” to a higher level of service than they actually provided.) EHRs encourate fraud in two ways: First, they make it easier to generate documentation that justifies higher-cost codes. Second, some providers have fraudulently attested to meaningful use. CMS has directed its auditors to pay more attention to EHR documentation and has begun random audits of providers who have attested to Meaningful Use.
Merit-Based Incentive Program (MIPS)
The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) replaces Medicare’s current method of paying physicians with two other approaches, both of which will take effect in 2019. One method gives physicians who participate in an “alternative payment model” such as an accountable care organization or a patient-centered medical home a 5 percent annual increase in their Medicare payments through 2024. Physicians who don’t participate in such an arrangement will be subject to MIPS, in which their Medicare payments will be adjusted upward or downward by 4 percent in 2019, increasing to 9 percent by 2022. Like the Value-Based Payment Program (see separate entry), MIPS uses quality and efficiency scores to determine whether physicians get a penalty or a bonus. About a third of the composite score is based on quality measures, which may reflect some factors outside physicians’ control, experts point out.
Mobile health, also called mHealth, encompasses mobile devices and applications used in healthcare. In this context, mobile devices include smartphones and tablets, as well as add-on devices. In some cases, smartphones can be converted into medical devices, such as an ECG or a stethoscope. Alternatively, they can be used in combination with add-on devices such as glucometers that transmit data via Bluetooth to a smartphone app. The overwhelming majority of mHealth apps are designed for wellness or fitness, such as diet and exercise apps. Consumers use most of these by themselves, but some apps allow users to share information with friends and family. Apps for monitoring chronic diseases have yet to gain much traction, mainly because most physicians are not yet willing to review the data. But some physicians are beginning to prescribe mHealth apps to their patients.
Some vendors provide either full mobile-native EHRs or apps that supply limited EHR functionality. One vendor’s mobile app, for example, allows doctors to retrieve visit notes, view diagnosis and medication lists, write prescriptions, and send secure email. The small screen size of smartphones and the clumsiness of onscreen keyboards are barriers to mobile EHRs. But speech recognition ameliorates the keyboarding issue in some of these EHR versions. Although there are mobile-native EHRs for Android tablets and smartphones, physicians favor iOS devices.
Natural language processing
Under development for half a century, natural language processing, or the capability of computers to understand human language, is finally coming into its own in healthcare. The use of NLP with speech recognition engines in EHRs has not been very successful, because those applications grasp only a limited number of medical concepts and are not reliable enough for physicians to use in clinical care. But the use of cognitive computing systems (see: Big data) has produced NLP applications that can understand language in context. This enables healthcare providers to convert some of the unstructured data that comprises about 80 percent of EHRs into structured data that is available for analysis.
PACS may serve all hospital departments or may be split among radiology, cardiology and other departments. These systems house radiology images and reports and may include “radiology information systems” (RIS) that handle patient scheduling, image tracking, and results reporting. But in recent years, many hospitals have turned off their RIS and have integrated PACS with EHRs for RIS functions. Multiple PACS within a hospital or across hospitals and outpatient imaging centers are hard to integrate. Moreover, storage demands are growing exponentially. So a number of healthcare organizations now use vendor-neutral archives (VNAs) to store images from disparate PACS. Clinicians can access the VNAs directly from EHRs. Alternatively, some organizations use enterprise viewers to retrieve images from multiple PACS.
Patient cost accounting systems
Cost accounting systems in hospitals record, analyze, and allocate costs to the individual services provided to patients, such as medications, procedures, tests, and room and board. These systems were once considered optional in healthcare. But in recent years, as value-based reimbursement has gathered momentum, most hospitals have started looking hard at their cost structure, from labor to supply chain costs. Physician costs are often measured in “relative value units,” which assign work values to particular professional services based on an agreed-upon national formula. Hospitals usually analyze their costs and revenues within departments such as cardiology and surgery or service lines such as heart centers and maternity centers.
Largely because of the EHR incentive program, it’s common for EHRs to have patient portals so that patients can view or download their records and message providers online. But those activities are still occurring only to a limited extent: For example, providers objected to the requirement in the government’s EHR incentive program that just 5 percent of their patients view their records online. Although patient portals could greatly increase healthcare efficiency and improve the quality of care, the potential of this technology has yet to be fully exploited.
Patient scheduling systems
Patient scheduling, known as registration on the hospital side, goes beyond simple appointment booking. For new patients, this is the part of the process in which “patient demographics” – including name, contact information, age, sex, and insurance – are documented. In some organizations, schedulers verify insurance at this stage, before the patient arrives at the healthcare facility. There are separate ambulatory care and inpatient registration systems, and most hospitals also have surgical scheduling systems. Because no-shows can be costly to healthcare providers, scheduling systems may be connected to third-party reminder systems that send automated phone messages to patients prior to office visits or scheduled tests or procedures.
Retail pharmacy systems bill insurance companies and pharmacy benefit managers for prescription drugs and check prescriptions for safety before pharmacists fill them. Most pharmacies use third-party drug databases that help them identify potentially adverse drug-to-drug and drug-to-allergy interactions. Retail pharmacy systems are connected online with physician offices through a company calls Surescripts, which is owned by the trade associations of the chain and independent pharmacies. Using Surescripts, physicians automatically transmit their electronic prescriptions into the pharmacy systems, eliminating errors due to poor handwriting and faulty data entry.
Physician performance measurement
In most physician groups, performance was traditionally measured in terms of productivity – either revenue- or RVU-based – that was reflected in each doctor’s compensation. But, as healthcare moves from pay for volume to pay for value, healthcare organizations are factoring quality and efficiency into physician pay. So they need financial systems that can not only track RVUs, but can also measure each provider’s utilization of resources, including supplies, tests, and staff time. Utilization management is especially important in organizations that are taking financial risk. Today, most programs that measure performance in this way are part of population health management solutions.
Physician Quality Reporting System (PQRS)
The Physician Quality Reporting System is the source of the data that the Centers for Medicare and Medicaid Services (CMS) uses to determine whether physicians should have their Medicare payments adjusted up or down as CMS phases in value-based payments for doctors (see separate entry). Most physicians who report their quality data to PQRS still use a claims-based method, but they’ll have to report electronically from their EHRs, starting in 2018. Today, many physicians who report electronically can do so either through a special clinical data registry (often operated by a specialty society) or can send the data directly to CMS. The latter approach requires dedicated software from an EHR vendor or an outside data submission vendor—both of which cost money. Practices of 25 or more physicians can use a Group Practice Reporting Option (GPRO) web interface to report. CMS recently aligned its meaningful use and PQRS measures so that practices only have to report their quality data once.
Picture archiving and communications system (PACS)
A PACS houses radiology images and reports in hospitals and standalone imaging centers. They may serve all hospital departments or may be split among radiology, cardiology and other departments. PACS often include “radiology information systems” (RIS) that handle patient scheduling, image tracking, and results reporting. But in recent years, many hospitals have turned off their RIS and have integrated PACS with EHRs for RIS functions. The images themselves are usually in a format called DICOM that is distinct from the databases used in EHRs. Also, different PACS are not interoperable. So some organizations use enterprise viewers embedded in their EHRs to view images in multiple PACS.
Population health management (PHM)
Population health management seeks to optimize the health of all patients and to prevent their chronic conditions from worsening. This approach involves the use of care teams, care coordination across care settings, continuous care, patient engagement techniques, care management of the sickest patients, and centralized resource planning. PHM requires the collection, aggregation and analysis of patient data from a variety of sources, some of it in near real time. The antithesis of the episodic “sick care” approach, PHM is essential to organizations that take financial risk for care.
Practice management (PM) systems
Most physician practices have PM systems that they use for scheduling, billing and financial accounting. Originally standalone, these systems were later integrated with EHRs and exchanged billing and patient demographic data across those interfaces. That is still true of less expensive EHRs and PM systems, but the leading vendors now integrate the clinical and practice management sides in a single application. That approach allows billing people, for example, to review clinical notes for coding purposes. Hospital financial systems are separate from the PM systems of hospital-owned practices, but the hospital’s central business office often handles billing and scheduling for those practices.
Predictive modeling is a type of analytics used to forecast the future health status of individuals and to classify patients by their current health risk (risk stratification). It can also be used to risk-adjust the aggregate health risks of a particular group of patients, such as a physician’s patient panel. This is important to healthcare organizations that are negotiating risk contracts, because they want to get paid more for caring for sicker patients. Predictive modeling is used to identify high-risk patients who need care management, to forecast which patients are most likely to incur high costs in the coming year, and to predict which patients are likely to be readmitted to the hospital. Most predictive modeling algorithms are based on claims data, which is the broadest dataset. However, clinical data is more timely and actionable and includes many elements missing from claims data.
Quality measurement and reporting
Government regulations require health care providers to report on quality measures, using either administrative or EHR data. Many private payers use claims data to evaluate the quality and cost of providers. To show meaningful use, providers must extract data from their EHRs. They may report it directly to CMS or use special registries for reporting. Because of the deficiencies of structured data in EHRs, many organizations must assign clinical staff to comb through patient records to locate the desired data. EHR vendors also have difficulty in programming their systems to meet CMS quality reporting requirements.
Patients don’t always see the specialists to whom their primary care doctors refer them, and specialists don’t always send reports on the patients they do see to the referring physicians. To close this information gap, some organizations use EHR modules or third party software that alert physicians when they have not received a report back from a specialist. Some hospitals use automated messaging applications that surveys recently discharged patients to find out, among other things, whether they have made an appointment to see a primary care physician. If not, a nurse will call the patient and refer them to a doctor in the organization if they don’t have one.
Patient registries show the services that have been provided to each patient, when that service was performed, and when people with particular conditions are due for follow-up visits or tests. They also include demographic information, lab results, and medications. Registries have analytics that can be applied to populations and subgroups, such as patients with diabetes or hypertension who have out-of-range lab values. While some EHRs include registries, they’re usually rudimentary and lack basic analytic tools. Robust registries, which may be standalone or incorporated into data warehouses are considered more useful in PHM.
Remote patient monitoring
Remote monitoring can be done via mobile apps or with home monitoring equipment. Nevertheless, most pilots of this technology have focused on home monitoring. Remote monitoring has been shown to have health benefits for patients with congestive heart failure (CHF), and many hospitals are interested in using it to prevent CHF patients from being readmitted. Some healthcare systems and health plans are also investigating the use of home monitoring in treating chronic diseases such as hypertension and diabetes as part of population health management. As noted earlier, most physicians are still not ready to accept data from mobile chronic disease apps. Few of these apps have been tested or approved by the FDA. Moreover, clinicians need better screening mechanisms to filter relevant data from the mobile data streams.
Revenue cycle management
Physician practices and hospitals do revenue cycle management (RCM) to maximize their revenue and minimize bad debt. The key elements of RCM are insurance eligibility verification, copayment collection, coding of diagnoses and procedures for billing, claims submission and tracking, payment posting, accounts receivable management, and reporting and benchmarking. Practice management and hospital patient accounting systems are often coupled with third-party solutions for certain RCM functions. Some healthcare organizations outsource RCM, which can expand their resources but is costly and requires them to give up some control. To reduce the amount of bad debt because of the inability to collect from patients who are uninsured or have high deductibles, some hospitals have installed software to locate alternative sources of payment, develop payment plans, and find financial assistance for those unable to pay.
Risk adjustment is a methodology used to compare the aggregate health risks of a physician’s or a healthcare organization’s patients or a health plan’s members to those of another doctor, healthcare entity, or insurance plan. The calculation of health risk is usually derived from claims data, which includes information on healthcare services, medications, age and gender. Risk adjustment may be used to make payments fairer or to help healthcare organizations benchmark their performance internally or against those of other organizations. Health plans also use risk adjustment models, such as the ACG Predictive Model from Johns Hopkins University, in provider profiling.
Risk management tools
While a relatively small number of healthcare organizations are now taking financial risk for care delivery, this method of payment is expected to spread in coming years as large healthcare systems and medical groups seek to maximize their return on investment in PHM infrastructure. Today, most risk-bearing provider entities outside of California are accountable care organizations (ACOs). ACOs use data warehouses and registries to aggregate and analyze data. They measure their own performance on quality and efficiency, and they use budgeting and forecasting tools to manage financial risk. When they partner with health plans, ACOs may also analyze claims data to track the movement of patients to non-network providers.
The classification of patients by health risk is a cornerstone of population health management. At the population level, risk stratification allows health leaders to monitor and track the health status of various subpopulations and to review the organization’s performance in caring for those groups. At the level of individual patients, risk stratification enables the organization to identify the patients who are likely to incur the highest health costs in any given year. Patients can be classified as low-, medium- and high-risk so that care teams can intervene to prevent people who have moderate chronic diseases from becoming acutely ill. This approach can reduce the number of costly ER visits and hospitalizations.
Patient portals can be used for secure messaging between physicians and patients. This technology, which goes back 15 years, usually requires patients to visit the portal to view their messages; in some cases, they may receive emails saying that a message from their provider is waiting for them. Patients can also use portals to ask questions about their health conditions, their care plans, or how to take their medications. Some physicians have discovered that patients don’t always check their messages on the portal, so they send them text messages.
Single sign on
The idea of logging on once to gain access to multiple systems is not unique to healthcare, but it has some specific implications in this industry. Because physicians and nurses are so busy and depend so much on their EHRs, there is a basic tension between health IT security and the ability of providers to do their jobs. Single sign-on speeds up their access to clinical systems, but it also creates some security vulnerabilities, such as weak or stolen credentials. Perhaps as a result, fewer than half of U.S. acute-care hospitals use single sign on systems. Some of the healthcare systems that do use it confine SSO access to enterprise workstations, requiring remote users to log on separately to each system.
The federal Stark law governs physician self-referral for Medicare and Medicaid patients. Self-referral occurs when a doctor refers a patient to a facility in which he or she has a financial interest, such as a lab, an ambulatory surgery center, or a hospital. Congress passed the original self-referral statute, sponsored by then Rep. Pete Stark (D-Calif.), in 1989. A second version of the Stark legislation, which contained more exceptions than the first, was adopted in 1992 and amended several times after that. The latest version, which passed in 2007, includes an exception that allows hospitals to subsidize physician purchases of EHR software up to 85 percent of the purchase price, as well as training costs. A related “safe harbor” also exists in the Anti-Kickback Act (AKA), which prohibits hospitals from providing anything of financial value to physicians in return for patient referrals. Originally scheduled to sunset at the end of 2013, the Stark exception and the AKA safe harbor have been extended through 2021.
Telehealth, sometimes called telemedicine, encompasses virtual visits, remote patient monitoring, and educational and support applications. There is a lot of overlap between telehealth and mHealth; however, telehealth includes home monitoring of health conditions, as well as online consultations and support materials that can be accessed on desktops and laptops with Internet connections. None of this has yet joined the mainstream of healthcare. But it is believed that the shift to value-based reimbursement will change that by paying doctors for non-visit care. Some experts predict that telehealth will change how healthcare is delivered. Not only will it reduce the number of unnecessary office visits, they say, but it will also allow doctors to monitor their patients’ health continuously.
In recent years, audio and/or video “virtual visits” between consumers and physicians have spread across the country (see the mobile and telehealth section). The majority of states now require private health plans to cover these visits in the same way that they would pay for office visits. Some states also provide some telehealth coverage through their Medicaid programs. To date, Medicare has declined to cover most telehealth services except in rural areas. The agency typically requires the patient to be in an office with a primary care physician who is consulting a specialist remotely. In contrast, private insurers cover services provided remotely to patients wherever they are, on computers or smartphones.
Value-based Payment Program
The Centers for Medicare and Medicaid Services (CMS) has embarked on a pay-for-performance program that financially rewards or penalizes hospitals, physicians and certain other professionals who participate in Medicare, based on their quality and efficiency scores. The hospital portion of this Value-based Payment Program began in October 2012. CMS started to apply a “value-based modifier” to the reimbursement of physicians in groups of 100 doctors or more in 2015, based on 2013 performance. In 2016, the modifier will be applied to groups of 10 or more doctors; in 2017, all physicians will be affected. The quality scores in this program are based on Physician Quality Reporting System (PQRS) data. Physicians who don’t participate in PQRS will lose 2 percent of their Medicare reimbursement in 2016. Starting in 2019, the value-based payment, PQRS and meaningful use programs will all be folded into the Merit-Based Incentive Program (MIPS) under a new law that changes how Medicare pays physicians.
Vendor-neutral archive (VNA)
Multiple PACS within a hospital or across hospitals and outpatient imaging centers are hard to integrate. Moreover, storage demands are growing exponentially. So a number of healthcare organizations now use vendor-neutral archives (VNAs) to store images from disparate PACS. Clinicians can access the VNAs directly from their EHRs. Data migration from PACS systems to VNAs can be tricky and can take anywhere from several months to a few years. In the interim, healthcare providers need continuous access to the stored images and reports. To make matters worse, some PACS store data in proprietary formats that make it hard to extract. Many healthcare organizations view VNAs as a vehicle to liberate their images from vendors that seek to lock them in.
Remote consultations, as noted earlier, have been growing rapidly and no longer just involve patients and physicians in rural areas. In recent years, many health plans and employers have contracted with telehealth services that provide 24/7 access to physicians via phone or video chat on smartphones and desktop/laptop computers. These can be combined with photos of relevant portions of the body. While the medical establishment opposes allowing doctors to diagnose and prescribe remotely to people they’ve never met, some healthcare organizations have quietly begun offering virtual visits to their own doctors’ patients.
One of the fastest growing areas of mHealth, wearable sensors track everything from activity to vital signs such as heart rate, metabolic rate, and heart rhythms. These sensors may be imbedded in wrist bands, chest patches, or other kinds of devices. Consumers use these wearables and their associated apps mostly to track their own health, but they could also be used for continuous monitoring of people with chronic conditions. As with add-on devices like glucometers and digital blood pressure cuffs, some wearables use Bluetooth to connect with smartphones. The consumer can then view the data and/or upload it to a data center in a healthcare organization.