Henry C. Chueh

Clinical inertia in the management of Type 2 diabetes metabolic risk factors.

Aims  Delays in the initiation and intensification of medical therapy may be one reason patients with diabetes do not reach evidence‐based goals for metabolic control. We assessed intensification of medical therapy over time, comparing the management of hyperglycaemia, hypertension, and hyperlipidaemia.

Instrumenting the health care enterprise for discovery research in the genomic era

Tens of thousands of subjects may be required to obtain reliable evidence relating disease characteristics to the weak effects typically reported from common genetic variants. The costs of assembling, phenotyping, and studying these large populations are substantial, recently estimated at three billion dollars for 500,000 individuals. They are also decade-long efforts. We hypothesized that automation and analytic tools can repurpose the informational byproducts of routine clinical care, bringing sample acquisition and phenotyping to the same high-throughput pace and commodity price-point as is currently true of genome-wide genotyping. Described here is a demonstration of the capability to acquire samples and data from densely phenotyped and genotyped individuals in the tens of thousands for common diseases (e.g., in a 1-yr period: N = 15,798 for rheumatoid arthritis; N = 42,238 for asthma; N = 34,535 for major depressive disorder) in one academic health center at an order of magnitude lower cost. Even for rare diseases caused by rare, highly penetrant mutations such as Huntington disease (N = 102) and autism (N = 756), these capabilities are also of interest.

Just-in-time clinical information.

The just-in-time (JIT) model originated in the manufacturing industry as a way to manage parts inventories process so that specific components could be made available at the appropriate times (that is, “just in time”). This JIT model can be applied to the management of clinical information inventories, so that clinicians can have more immediate access to the most current and relevant information at the time they most need it--when making clinical care decisions. The authors discuss traditional modes of managing clinical information, and then describe how a new, JIT model may be developed and implemented. They describe three modes of clinician-information interactions that a JIT model might employ, the scope of information that may be made available in a JIT model (global information or local, case-specific information), and the challenges posed by the implementation of such an information-access model. Finally, they discuss how JIT information access may change how physicians practice medicine, various ways JIT information may be delivered, and concerns about the trustworthiness of electronically published and accessed information resources.

Predictors of antiretroviral treatment failure in an urban HIV clinic

Background:Predictors of antiretroviral treatment (ART) failure are not well characterized for heterogeneous clinic populations. Methods:A retrospective analysis was conducted of HIV-infected patients followed in an urban HIV clinic with an HIV RNA measurement ≤400 copies/mL on ART between January 1, 2003, and December 31, 2004. The primary endpoint was treatment failure, defined as virologic failure (≥1 HIV RNA measurement >400 copies/mL), unsanctioned stopping of ART, or loss to follow-up. Prior ART adherence and other baseline patient characteristics, determined at the time of the first suppressed HIV RNA load on or after January 1, 2003, were extracted from the electronic health record (EHR). Predictors of failure were assessed using proportional hazards modeling. Results:Of 829 patients in the clinic, 614 had at least 1 HIV RNA measurement ≤400 copies/mL during the study period. Of these, 167 (27.2%) experienced treatment failure. Baseline characteristics associated with treatment failure in the multivariate model were: poor adherence (hazard ratio [HR] = 3.44; 95% confidence interval [CI]: 2.34 to 5.05), absolute neutrophil count <1000/mm3 (HR = 2.90, 95% CI: 1.26 to 6.69), not suppressed on January 1, 2003 (HR = 2.69, 95% CI: 1.78 to 4.07) or <12 months of suppression (HR = 1.64, 95% CI: 1.10 to 2.45), CD4 count <200 cells/mm3 (HR = 1.90, 95% CI: 1.31 to 2.76), nucleoside-only regimen (HR = 1.75, 95% CI: 1.08 to 2.82), prior virologic failure (HR = 1.70, 95% CI: 1.22 to 2.39) and ≥1 missed visit in the prior year (HR = 1.56, 95% CI: 1.13 to 2.16). Conclusions:More than one quarter of patients in a heterogeneous clinic population had treatment failure over a 2-year period. Prior ART adherence and other EHR data readily identify patient characteristics that could trigger specific interventions to improve ART outcomes.

Randomized controlled trial of an informatics-based intervention to increase statin prescription for secondary prevention of coronary disease

OBJECTIVE: Suboptimal treatment of hyperlipidemia in patients with coronary artery disease (CAD) is well documented. We report the impact of a computer-assisted physician-directed intervention to improve secondary prevention of hyperlipidemia.DESIGN AND SETTING: Two hundred thirty-five patients under the care of 14 primary care physicians in an academically affiliated practice with an electronic health record were enrolled in this proof-of-concept physician-blinded randomized, controlled trial. Each patient with CAD or risk equivalent above National Cholesterol Education Program-recommended low-density lipoprotein (LDL) treatment goal for greater than 6 months was randomized, stratified by physician and baseline LDL. Physicians received a single e-mail per intervention patient. E-mails were visit independent, provided decision support, and facilitated “one-click” order writing.MEASUREMENTS: The primary outcomes were changes in hyperlipidemia prescriptions, time to prescription change, and changes in LDL levels. The time spent using the system was assessed among intervention patients.RESULTS: A greater proportion of intervention patients had prescription changes at 1 month (15.3% vs 2%, P=.001) and 1 year (24.6% vs 17.1%, P=.14). The median interval to first medication adjustment occurred earlier among intervention patients (0 vs 7.1 months, P=.005). Among patients with baseline LDLs >130 mg/dL, the first postintervention LDLs were substantially lower in the intervention group (119.0 vs 138.0 mg/dL, P=.04). Physician processing time was under 60 seconds per e-mail.CONCLUSION: A visit-independent disease management tool resulted in significant improvement in secondary prevention of hyperlipidemia at 1-month postintervention and showed a trend toward improvement at 1 year.

A self-scaling, distributed information architecture for public health, research, and clinical care.

OBJECTIVE This study sought to define a scalable architecture to support the National Health Information Network (NHIN). This architecture must concurrently support a wide range of public health, research, and clinical care activities. STUDY DESIGN The architecture fulfils five desiderata: (1) adopt a distributed approach to data storage to protect privacy, (2) enable strong institutional autonomy to engender participation, (3) provide oversight and transparency to ensure patient trust, (4) allow variable levels of access according to investigator needs and institutional policies, (5) define a self-scaling architecture that encourages voluntary regional collaborations that coalesce to form a nationwide network. RESULTS Our model has been validated by a large-scale, multi-institution study involving seven medical centers for cancer research. It is the basis of one of four open architectures developed under funding from the Office of the National Coordinator of Health Information Technology, fulfilling the biosurveillance use case defined by the American Health Information Community. The model supports broad applicability for regional and national clinical information exchanges. CONCLUSIONS This model shows the feasibility of an architecture wherein the requirements of care providers, investigators, and public health authorities are served by a distributed model that grants autonomy, protects privacy, and promotes participation.

Internet Use Among Primary Care Patients with Type 2 Diabetes

AbstractBACKGROUND: The Internet represents a promising tool to improve diabetes care. OBJECTIVE: To assess differences in demographics, self-care behaviors, and diabetes-related risk factor control by frequency of Internet use. DESIGN AND PARTICIPANTS: We surveyed 909 patients with type 2 diabetes attending primary care clinics. MEASUREMENTS: Frequency of Internet use, socioeconomic status, and responses to the Problem Areas in Diabetes (PAID), Summary of Diabetes Self-care Activities (SDSCA), and Health Utilities Index (HUI) scales. Survey responses were linked to last measured hemoglobin Alc, cholesterol, and blood pressure results. Comorbidities and current medications were obtained from the medical record. RESULTS: Internet “never-users” (n=588, 66%) were significantly older (70.0±11.2 vs 59.0±11.3 years; P<.001) and less educated (26% vs 71% with > high school; P<.001) than Internet users (n=308, 34%). There were few significant differences in PAID or SDSCA scores or in diabetes metabolic control despite longer diabetes duration (10.3±8.2 vs 8.3±6.7 years; P<.001) and greater prevalence of coronary disease (40% vs 24%; P<.001) in nonusers. Less than 10% of current nonusers would use the Internet for secure health-related communication. CONCLUSIONS: Older and less educated diabetes patients are less likely to use the Internet. Despite greater comorbidity, nonusers engaged in primary care had equal or better risk factor control compared to users.

Linking Electronic Health Record-Extracted Psychosocial Data in Real-Time to Risk of Readmission for Heart Failure

BACKGROUND Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care. OBJECTIVE We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days. METHODS We identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission. RESULTS We found five characteristics-dementia, depression, adherence, declining/refusal of services, and missed clinical appointments-that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source. CONCLUSIONS Unstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.

Automated coded ambulatory problem lists: evaluation of a vocabulary and a data entry tool

BACKGROUND Problem lists are fundamental to electronic medical records (EMRs). However, obtaining an appropriate problem list dictionary is difficult, and getting users to code their problems at the time of data entry can be challenging. OBJECTIVE To develop a problem list dictionary and search algorithm for an EMR system and evaluate its use. METHODS We developed a problem list dictionary and lookup tool and implemented it in several EMR systems. A sample of 10,000 problem entries was reviewed from each system to assess overall coding rates. We also performed a manual review of a subset of entries to determine the appropriateness of coded entries, and to assess the reasons other entries were left uncoded. RESULTS The overall coding rate varied significantly between different EMR implementations (63-79%). Coded entries were virtually always appropriate (99%). The most frequent reasons for uncoded entries were due to user interface failures (44-45%), insufficient dictionary coverage (20-32%), and non-problem entries (10-12%). CONCLUSION The problem list dictionary and search algorithm has achieved a good coding rate, but the rate is dependent on the specific user interface implementation. Problem coding is essential for providing clinical decision support, and improving usability should result in better coding rates.

The computer-based clinical record--where do we stand?

The practice of medicine depends on how we record, process, retrieve, and communicate information. Physicians are often frustrated with the inadequacies and duplication of the existing paper-based medical record and with the time wasted in locating medical information. The recognition of the need for more comprehensive and available documentation of patient care is not new. In her 1863 book Notes on a Hospital, Florence Nightingale wrote, In attempting to arrive at the truth, I have applied everywhere for information, but in scarcely an instance have I been able to obtain hospital records fit for any purposes of comparison. If they could be obtained they would show subscribers how their money was being spent, what amount of good was really being done with it, or whether the money was not doing mischief rather than good [1]. The need to improve the management of medical information is more critical now because of the explosion of medical knowledge and because of the need to provide comprehensive documentation of patient care for an ever-growing list of interested parties [2]. The article by van der Lei and colleagues in this issue of Annals [3], which describes the introduction of computer-based records in the Netherlands, is encouraging. The authors report that more than one fourth of the 6400 Dutch general practitioners have instituted a computer-based clinical record system in their offices. We discuss the status and issues involved with the use of computer-based records in the United States in the context of the Dutch experience. The diverse and heterogenous patterns of care and the various medical provider and institutional environments characteristic of U.S. health care have resulted in a fragmented patient medical record, with no single provider, institution, or third-party payer responsible for maintaining a comprehensive record. Consequently, the major thrust of the successful computer-supported medical information systems in the United States has been to support the financial, administrative, and communication functions of individual institutions. The primary emphasis of these computer systems has not been the clinical record but functions such as billing, admission and discharge, scheduling, and laboratory reporting. Some systems have incorporated the retrieval of previously transcribed clinical data such as radiology reports, discharge summaries, problem lists, and visit notes. Once stored in the computer-based clinical record system, the data can be viewed simultaneously by multiple persons at different sites. Impressive examples of such systems are in place both in U.S. hospitals [4-7] and in some ambulatory practices [4, 7-10]. For example, several hundred ambulatory care sites use COSTAR, a public domain computer-based ambulatory medical record system developed at Massachusetts General Hospital [8, 9]. In COSTAR, the medical data for a patient visit are recorded on a paper-based encounter form and then transcribed into the computer system by clerical personnel. Other examples of computer-based systems include the electronic record developed at the Regenstrief Institute at Indiana University [4], where a clinician can view a patients problem list and laboratory data interactively as flowsheets, allowing easier detection of trends. An ambulatory computer-based record at Bostons Brigham and Womens Hospital [10] also provides a summary screen displaying a patient-at-a-glance with a problem list, allergies, and medications. In these systems, like many similar systems, the patient information is accessed either through direct inquiry at a computer terminal or through computer-generated summaries and reports. A major impediment to the development of a computer-based clinical record system has been the lack of agreement in standards both for the clinical terminology to be used and for the computer technology. The American Society for Testing and Materials (ASTM) recently promulgated a standard to describe the content and structure of a computer-based system [11], but it is not widely reflected in currently used systems. A consortium of vendors and hospitals, Health Level Seven, is developing standards for transmitting billing; admission, discharge, and transfer; order entry; and the reporting of results between a network of computers [12]. Health Level Seven collaborates closely with ASTM and has defined a standard for the protocol to be used in the communication of laboratory data in an electronic format; this standard has been adopted by many vendors. The absence of standards for the other sections of the clinical record and the lack of support for standards by the government and professional organizations has resulted in the use of many competing computer operating systems, hardware platforms, user interfaces, and software tools, making every computer-based record system implementation almost hopelessly proprietary. Little attention has been focused on establishing a standard for a system with an open architecture, a standard that would allow computer hardware and software products of many different manufacturers to function together. Given the diversity of computer technology, such an open architecture will be essential for the dissemination and national adoption of a common computer-based system. The successful development of the automated medical record in the Dutch system is based largely on the countrys progress in four crucial areas [9, 13, 14]. These are the development of a standard clinical vocabulary, effective methods for direct physician interaction with the computer-based system, support of key professional societies, and judicious use of government funding. Standard Clinical Vocabulary One clear advantage of computer-based medical records is that clinical information can be retrieved almost instantaneously and simultaneously from many different sites. Simply recording and storing the medical information in a narrative format in a computer system would accomplish this and would improve the legibility of previously handwritten notes. However, using the computer as nothing more than a large word-processing program would make analysis of the nature, extent, and time-course of the disease processes, therapies, and outcomes difficult and time consuming. Without a more structured method of data collection using a standard controlled vocabulary, it will be impossible to make rational decisions about which treatments are cost-effective and how patient outcomes are affected by our clinical decisions. A controlled vocabulary implies a standard set of common terms (including accepted synonyms and abbreviations) for recording clinical information. A controlled vocabulary would allow the computer to search and sort data quickly, summarize the information (for example, the active problems and active medications), identify important clinical manifestations (such as a drug allergy), and retrieve selectively (for example, the last visit note discussing the problem of congestive heart failure). In addition, a structured clinical record with a controlled vocabulary would enable the computer to identify and retrieve relevant medical knowledge (such as the contraindications of a particular drug), to provide problem-specific guidelines, to alert the provider to the need for indicated preventive medicine interventions, and to highlight an anomalous test result. Most commercial medical record systems (as well as the Elias system described in the article by van der Lei and colleagues) do not require a controlled vocabulary. The development and national adoption of a controlled vocabulary is a prerequisite for a computer-based clinical record to achieve its full potential [15]. Direct Physician Interaction with the Computer System For maximum effectiveness, the computer-based record system must completely replace the paper record, and the clinician responsible must interact directly with the computer to enter and retrieve medical information. This would reduce transcription errors as well as personnel costs and minimize delay in the availability of clinical information. An additional advantage of direct physician use of the computer-based system would be the provision of reminders and warnings when a clinical decision is being made. For example, a physician writing a prescription interactively can be warned of an adverse drugdrug interaction before the medication is given to the patient and possible harm ensues. This capability, which cannot be provided in a paper-based record, is a key advantage of a computer-based record and is supported by some hospital information systems in the United States. Changing from narrative text recording (either written or dictated) to interacting with a computer requires modification of longstanding traditions of medical recordkeeping. Although many medical students and young physicians have extensive experience using computer technology, there is little continuation of this in their training years. In addition, many older physicians, unfamiliar with the technology, are reluctant to enter data directly into the computer. Again, it is encouraging to note the progress toward this goal in the Dutch system. System designers continue to improve computer interfaces to permit rapid direct entry of narrative data into the computer while still maintaining the flexibility of the dictated note. For example, PEN&PAD, a prototype workstation being developed at the University of Manchester [16] in the United Kingdom, uses a graphic interface, user-defined templates for common clinical problems, and point-and-click technology to permit the user to enter rapidly a description of a patients symptoms and physical findings. In another example, interactive encoding of diagnoses by physicians has been a part of the electronic medical record at the University of Geneva Hospital in Switzerland since 1985 [17]. Despite such advances, the design of interfaces for direct entry of clinical information