Characterization of adult obesity in Florida using the OneFlorida clinical research consortium.

INTRODUCTION: With obesity rates and obesity-related healthcare costs increasing, policy makers must understand the scope of obesity across populations. OBJECTIVE: This study sought to characterize adult obesity using electronic health records (EHRs) available from a statewide clinical data research network, the OneFlorida Clinical Research Consortium, which contains claims and EHR data from over 12 million patients in Florida. The primary aim was to compare EHR-based Florida obesity rates with those rates obtained from the Behavioural Risk Factor Surveillance System (BRFSS). METHODS: Body mass index from OneFlorida patient data (2012-2016) was used to characterize obesity among adults 20-79 years old. Obesity rates from both OneFlorida and BRFSS (2013) were reported by demographics and by county. RESULTS: Among the 1,344,015 adults in OneFlorida with EHR data and who met inclusion criteria, the obesity rate was 37.1%. Women had higher obesity rates compared with men. Obesity rates varied within racial/ethnic groups, with the highest rate among African-Americans (45.7%). Obesity rates from OneFlorida were consistently higher than those found in BRFSS (overall 27.8%). CONCLUSIONS: Utilizing clinical big data available through hospital system and health partner collaborations provides an important view of the extent of obesity. Although these data are available only from healthcare users, they are large in scope, directly measured and are available sooner than commonly used national data sources.

The accuracy of medication data in an outpatient electronic medical record.

OBJECTIVE: To measure the accuracy of medication records stored in the electronic medical record (EMR) of an outpatient geriatric center. The authors analyzed accuracy from the perspective of a clinician using the data and the perspective of a computer-based medical decision-support system (MDSS). DESIGN: Prospective cohort study. METHODS: The EMR at the geriatric center captures medication data both directly from clinicians and indirectly using encounter forms and data-entry clerks. During a scheduled office visit for medical care, the treating clinician determined whether the medication records for the patient were an accurate representation of the medications that the patient was actually taking. Using the available sources of information (the patient, the patient's vials, any caregivers, and the medical chart), the clinician determined whether the recorded data were correct, whether any data were missing, and the type and cause for each discrepancy found. RESULTS: At the geriatric center, 83% of medication records represented correctly the compound. dose, and schedule of a current medication; 91% represented correctly the compound. 0.37 current medications were missing per patient. The principal cause of errors was the patient (36.1% of errors), who misreported a medication at a previous visit or changed (stopped, started, or dose-adjusted) a medication between visits. The second most frequent cause of errors was failure to capture changes to medications made by outside clinicians, accounting for 25.9% of errors. Transcription errors were a relatively ucommon cause (8.2% of errors). When the accuracy of records from the center was analyzed from the perspective of a MDSS, 90% were correct for compound identity and 1.38 medications were missing or uncoded per patient. The cause of the additional errors of omission was a free-text "comments" field-which it is assumed would be unreadable by current MDSS applications-that was used by clinicians in 18% of records to record the identity of the medication. CONCLUSIONS: Medication records in an outpatient EMR may have significant levels of data error. Based on an analysis of correctable causes of error, the authors conclude that the most effective extension to the EMR studied would be to expand its scope to include all clinicians who can potentially change medications. Even with EMR extensions, however, ineradicable error due to patients and data entry will remain. Several implications of ineradicable error for MDSSs are discussed. The provision of a free-text "comments" field increased the accuracy of medication lists for clinician users at the expense of accuracy for a MDSS.

Accuracy of data in computer-based patient records.

Data in computer-based patient records (CPRs) have many uses beyond their primary role in patient care, including research and health-system management. Although the accuracy of CPR data directly affects these applications, there has been only sporadic interest in, and no previous review of, data accuracy in CPRs. This paper reviews the published studies of data accuracy in CPRs. These studies report highly variable levels of accuracy. This variability stems from differences in study design, in types of data studied, and in the CPRs themselves. These differences confound interpretation of this literature. We conclude that our knowledge of data accuracy in CPRs is not commensurate with its importance and further studies are needed. We propose methodological guidelines for studying accuracy that address shortcomings of the current literature. As CPR data are used increasingly for research, methods used in research databases to continuously monitor and improve accuracy should be applied to CPRs.

Common medical terminology comes of age, Part One: Standard language improves healthcare quality.

It has become abundantly clear that standards of recording clinical terms in human-readable, computer-processable format are indispensable. Controlled medical terminology is the missing link in health information standards (in fact, medical terminology can be viewed as the mother of all standards); its absence interferes with the business of healthcare and impedes the core processes of healing and maintaining health. Medicine has lacked the controlled common medical vocabulary that would enable universal sharing of data at the point of care and ensure reliable information for determining health intervention effectiveness. Simple clinical and code content alone has proven insufficient for healthcare enterprises to successfully manage the terminology problem; the "lexical runtime engine," formerly called a vocabulary server (VOSER), which manages the vocabulary ontology and serves up the relevant vocabulary to users of applications in the clinical environment, has recently become a reality.

Common medical terminology comes of age, Part Two: Current code and terminology sets--strengths and weaknesses.

A number of clinical coding and vocabulary schemes are in use in healthcare enterprises today. Most of them are weak in light of the qualities that characterize adequate controlled medical terminologies, as outlined in Part One of this review. Here we look at the major code and terminology sets with a critical eye, as well as suggest practical steps to enable health industry information system purchasers and users to move forward with their effort to use common terminology to improve the quality, service, and knowledge in their enterprise.

Using belief networks to enhance sharing of medical knowledge between sites with variations in data accuracy.

Differences in data definition between sites are a known obstacle to sharing of reminder-system rule sets. We identify another data characteristic--data accuracy--with implications for sharing. We reviewed the literature on data accuracy and found reports of high error rates for many data classes used by reminder systems (e.g., problem lists). The accuracy of other, equally important, data classes had not been characterized. Wide variations in accuracy between sites has been observed, suggesting that such differences may pose a previously unrecognized barrier to sharing of reminder rules. We propose a belief-network model for encoding reminder rules that explicitly models site-specific data accuracy and we discuss how encoding knowledge in this format may lower the cost and effort required to share reminder rules between sites.

Optimal use of communication channels in clinical event monitoring.

We argue that the optimal use of communication channels in clinical event monitors is an important design consideration for these systems. We review the state-of-the-art in selection of communication channels, including our current approach--allowing users to choose the communication channel by which the event monitor sends each notification. We describe a new approach that we are in the process of developing. In this new approach, we view event monitoring as the decision of whether and how to send new patient data to a clinician and apply the principle of maximum expected utility to this decision problem. Our initial experience with this approach suggests that notifying clinicians of normal patient data may be of high utility. We also found that methods for explanation in uncertain reasoning may be necessary in this approach.

Free-text fields change the meaning of coded data.

Researchers have advocated the supplementation of coded fields with free-text fields in electronic medical records (EMRs) to provide clinicians with flexibility during data entry. They cite advantages of more complete data capture and improved clinician acceptance and use of the EMR. However, free text may have the disadvantage of changing the meaning of coded data, which causes lower data accuracy for applications that cannot read free text. We studied the free-text entries that clinicians made during the recording of medication data. We found that these entries changed the meaning of coded data and lowered data accuracy for the medical decision-support system (MDSS) in our EMR. We conclude that supplemental free-text entries made by clinicians frequently alter the meaning of coded data.

The use of an explanation algorithm in a clinical event monitor.

Clinical event monitors (CEMs) seek to improve patient care and reduce its cost by prompting clinicians to take actions that have these effects. To persuade clinicians to act, CEMs have used prewritten-text explanations. However, we encountered limitations of prewritten-text explanations in our CEM. Therefore, we decided to implement an advanced method for explanation (Suermondt's method for belief-network explanation). This method is promising, but whether it is generally applicable to all of clinical event monitoring and whether it is as efficacious as prewritten-text explanations remain areas for future research.

Preferences of interns and residents for E-mail, paging, or traditional methods for the delivery of different types of clinical information.

We elicited from medical house staff their preferences for e-mail and alphanumeric pager as communication channels for the delivery of 18 different types of clinical information about their inpatients. For each type, we calculated the proportion of users who preferred delivery by e-mail, pager, both, or neither (usual delivery). For 14/18 (78%) types, more users preferred delivery by pager than by the other options. For 2/18 (11%) types, e-mail was preferred. For 2/18 (11%) types, more users preferred redundant delivery using both channels. For no types did more users prefer neither, meaning that the information would be delivered by traditional channels, if any. We conclude that medical house staff in the inpatient setting prefer to receive many types of clinical information by pager. The reason may be that they otherwise would have to query clinical information systems for these data, which is wasteful of their time and introduces delays into the process of care. Additionally, we found significant inter-user variability, suggesting that it may be useful for the notification services of an enterprise to employ user profiles for the delivery of clinical information.

A feasibility study of two methods for end-user configuration of a clinical event monitor.

We developed and evaluated a feature that allows users to control what types of clinical information are delivered to them. Using a paper or web-based configuration form, users turn individual alerts and sets of results on or off, and set how they are delivered. We used usage rates to evaluate this feature. Of 16 residents who had received clinical information from our clinical event monitor, 4 (25%) made at least one change (range 10-25). Of 41 interns, 5 (12.2%) made at least one change (range 5-91). The difference was borderline significant (p < 0.1). 5/7 web users changed preferences through a dial-up connection from home. More users used the web-based preference form than the paper form. This difference may be due to the better accessibility of the web-based form. A survey established that this feature was not as highly utilized as anticipated partly because the initial (default) preference setting was acceptable and partly because the users were too busy to customize their alert settings. We conclude that user configuration of a system that delivers information using a web-based preference form is feasible and may become important as the volume of information and number of available communication channels increase.

Mobile workers in healthcare and their information needs: are 2-way pagers the answer?

The ability to have access to information relevant to patient care is essential within the healthcare environment. To meet the information needs of its workers, healthcare information systems must fulfill a variety of functional requirements. One of these requirements is to define how workers will interact with the system to gain the information they need. Currently, most healthcare information systems rely on users querying the system via a fixed terminal for the information they desire; a method that is inefficient because there is no guarantee the information will be available at the time of their query and it interrupts their work flow. In general, clinical event monitors--systems whose efficacy relies on the delivery of time-critical information--have used e-mail and numeric pagers as their methods to deliver information. Each of these methods, however, still requires the user to perform additional steps, i.e., log into an information system in order to attain the information about which the system is alerting them. In this paper we describe the integration and use of 2-way alphanumeric pagers in CLEM, the UPMC Health System's Clinical Event Monitor, and how the use of these pagers addresses the information needs of mobile workers in healthcare.