through full narrative entries or scanned copies of notes. The initial term for these records was “electronic medical records” (EMRs) in that they dealt with the record of disease and interventions to cure them. However, as the repository came to include elements of health- care maintenance and preventative care items, it became a full “health record” spanning the continuum of care, hence becoming an “electronic health record,” or EHR. Elements in the following key areas made online records more advantageous3:
Accessibility.
• By making records available through applications distributed on desk- tops across an enterprise linked to a central server or by using an Internet-based application, patient data became available at any time and nearly any location, with- out the need for medical records file rooms and file clerks, thereby diminishing the specter of a lost record.
Legibility.
• The high degree of variability in provider handwriting has often been iden- tified as among the root causes of medication errors.4,5 The EHR circumvents this by
using a typewritten interface and by limiting the kinds of data that are allowable (see Chapter 4). These goals are accomplished by restricting the range of a numeric value to prevent “keying” errors and by making providers choose from a list of allowable values rather than entering variable text.
Use of discrete data.
• By forcing the use of discrete data elements in representation
of the elements of history, medical problems, medications, and even social history documentation, the record allows for aggregation of data across populations and the creation of association with other findings such as laboratory values or radiological findings. A good example of this is the correlation of certain disease states with the appropriate prescribing of classes of medications. Patients with left ventricular heart failure should be prescribed an ACE-inhibitor or angiotensin receptor blocker. If the presence of a left ventricular heart failure is detected but one of these medications is not on the active medication list, the patient’s physician can be prompted to prescribe one by sending an electronic alert to him or her.
The first use of EMRs occurred in single institutions that sought to develop systems to support the “business enterprises” of hospitals—namely, the capture of physician orders and their appropriate routing to departments such as laboratory, pharmacy, and radiology and then to processing the associated fees that should be charged for those services. The side benefit of these systems is that they often provided clinicians with access to results (e.g., laboratory and radiology reports) electronically, allowing them to move beyond paper printouts. The manner by which these results were aggregated formed the basis of the first tenet of comprehensive electronic records: an integrated view of patient data across time and specialty.
Individual systems often used proprietary formats to display laboratory and radiology values, which were very useful and tailored for their own systems but were not under- standable to a larger interface. Drawing on the experience of early programmers who leveraged the application programming interface (API) and standards initially created by the American Society for Testing and Materials (ASTM), developers learned to cre- ate universal messaging protocols to allow for interchange of these data among different computer systems.
Thus, individual hospitals would not be required to do interface programming them- selves when they sought to integrate individual departmental systems to their core EMR. This standard, now called health level 7 (HL-7), references the highest level of integration of information and is the industry leader. It is in use at over 1,500 healthcare institutions in the United States.6 Initially, this allowed providers to see relevant clinical
information for a particular inpatient stay. The standard has emerged to allow review of clinical information across time and across modality (see Figure 6.1).
To represent data elements, it also became important to standardize not only the ways in which data were transmitted, but also the way in which each individual data item was rendered to achieve the vision of “discrete” data as identified before. Terminologies were created to control the representation of data, each for its own individual area: diagnoses,
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procedures, psychiatric diagnoses, clinical observations (e.g., laboratory values, vital signs), and medications. The review of all of these is beyond the scope of this chapter (see Chapter 4 for details), but it is helpful to outline the most common standards used to represent these elements in electronic health records.
6.2.1 Diagnoses
Diagnoses are usually managed by the International Classification of Diseases and its Clinical Modifications (ICD-9-CM), which serves as the “lingua franca” of diagnostic terms in U.S. hospitals. It is used for clinical decision support and for billing support pur- poses. It is now out of sync with the rest of the world, which has moved to ICD version 10, which is slated to be implemented in the United States by 2013.
To aggregate a group of related diagnoses, the concept of diagnosis-related groups (DRGs) was created; this allows for a smaller number of diagnostic groups to define a given hospitalization, facilitating reimbursement for similar care across hospitals. For example, there are many ICD-9 terms for bacterial pneumonia (e.g., 482.83: pneumonia secondary to Gram-negative organisms, and 482.31: pneumonia secondary to streptococcus). However, many of them are rolled up into larger groups to create a more rational basis for compen- sating hospitals (e.g., DRG 89: pneumonia with complications, and DRG 90: pneumonia without complications).
6.2.2 utilization and Procedures
The American Medical Association keeps a master dictionary of procedures (Current
Procedural Terminology) that encompasses the universe of diagnostic and therapeutic proce-
dures done by providers to patients.7 Although its use is almost exclusively in the reimburse-
ment realm, it has uses among health services researchers to understand patterns of care.
FIGuRE 6.1 Review of results from many modalities, labs, and radiology across time. (Courtesy of
Furthermore, it is often the way in which requests for procedures (i.e., a laboratory or radiol- ogy test) are “ordered” by the core electronic medical record to the recipient ancillary system. 6.2.3 Laboratory Findings and Observations
Although it is not broadly deployed, researchers at the Regenstrief Institute in Indianapolis, Indiana, developed a system of structured data for laboratory findings and later for other observations (e.g., vital signs, electrocardiographic findings). This came to be known as the Logical Observation Identifier Names and Codes (LOINC) terminology.8
6.2.4 nursing Terminologies
To provide structure to nursing documentation at the bedside, a schema of problem (unique to direct bedside care) catalogs of the expected outcomes and the interventions to achieve those outcomes were developed. The most broadly deployed are the North American Nursing Diagnosis Association (NANDA), Nursing Outcomes Classification (NOC), and Nursing Interventions Classification (NIC).9 However, it is key to note that there is no
correlation between the more classical “medical” diagnoses utilized in ICD-9-CM and these nursing diagnoses. Consequently, most inpatient records have two problem lists at any time: those identified by the physician providers and those laid out by the nursing professionals. As more integrated EHRs are implemented, these disparate problem lists will likely need to be harmonized for the sake of interdisciplinary care.
6.2.5 Drug Codes
The challenge in creating structured terminology coding for medications is that the cur- rent standard from the U.S. Food and Drug Administration (FDA) is a dictionary enti- tled the National Drug Codes (NDCs) that is driven by the manufacturer and not unique to a specific drug, dose, and route. Rather, it is very much influenced by manufacturer and packaging. Although the National Library of Medicine (NLM) has sought to cre- ate a universal standard for transmitting medication information (RxNorm), to date it has not been widely adopted commercially. Most hospital systems rely on commercially prepared, proprietary drug databases with attached clinical decision support informa- tion (e.g., checking drug–allergy interaction and drug–drug interaction). The two most common providers in the United States are First Data Bank (San Bruno, California) and Medi-Span (Indianapolis, Indiana).
6.2.6 Implementation of EHRs
Structured terminologies and the manner to link these elements between ancillary systems and the core EMRs could enable institutions and, eventually, vendors to tackle the issue of using EHRs not only to display data, but also to capture observations, notes, and charges and to provide real-time decision support to clinicians. Initial records that emerged from early hospital information systems were pioneered at several academic medical centers in the 1980s and 1990s. Among the most notable were the HELP system developed at the LDS Hospital (Salt Lake City, Utah) and the Regenstrief System developed at Wishard Memorial Hospital (Indianapolis, Indiana). These systems were the first to use the rendering of
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clinical data, integrated from several systems, to provide physicians with clinical “guid- ance” in their orders.10
The development of these systems was assisted by being completely under the control of their local developers and being implemented in one clinical setting. This allowed for careful tailoring of the work flow to the local customs and resulted in high user adoption. Furthermore, use of the electronic medical record in an academic medical center with a relatively homogenous physician population—all affiliated with that center—also contrib- uted to improved adoption of the record. As recently as 2009, EHR availability and adop- tion were found to be positively correlated with larger practices.11
The key driver behind adoption and the move to the use of vendor-created EHRs has been increased recognition that the healthcare environment is fraught with potential errors and that a systematic approach to care, rather than the individual choice of a given provider, is more likely to result in a beneficial outcome to the patient.12 This realization
led many hospitals and large physician practice groups to consider adoption of electronic records; the composition of most records as well as their track record in improving care will be the next focus of this chapter. (Further discussion of EHR implementation issues can be found in Chapter 18.)