Paul R. Dexter

The Regenstrief Medical Record System: a quarter century experience

Entrusted with the records for more than 1.5 million patients, the Regenstrief Medical Record System (RMRS) has evolved into a fast and comprehensive data repository used extensively at three hospitals on the Indiana University Medical Center campus and more than 30 Indianapolis clinics. The RMRS routinely captures laboratory results, narrative reports, orders, medications, radiology reports, registration information, nursing assessments, vital signs, EKGs and other clinical data. In this paper, we describe the RMRS data model, file structures and architecture, as well as recent necessary changes to these as we coordinate a collaborative effort among all major Indianapolis hospital systems, improving patient care by capturing city-wide laboratory and encounter data. We believe that our success represents persistent efforts to build interfaces directly to multiple independent instruments and other data collection systems, using medical standards such as HL7, LOINC, and DICOM. Inpatient and outpatient order entry systems, instruments for visit notes and on-line questionnaires that replace hardcopy forms, and intelligent use of coded data entry supplement the RMRS. Physicians happily enter orders, problems, allergies, visit notes, and discharge summaries into our locally developed Gopher order entry system, as we provide them with convenient output forms, choice lists, defaults, templates, reminders, drug interaction information, charge information, and on-line articles and textbooks. To prepare for the future, we have begun wrapping our system in Web browser technology, testing voice dictation and understanding, and employing wireless technology.

Open-label randomized trial of torsemide compared with furosemide therapy for patients with heart failure

PURPOSE Because the bioavailability of oral furosemide is erratic and often incomplete, we tested the hypothesis that patients with heart failure who were treated with torsemide, a predictably absorbed diuretic, would have more favorable clinical outcomes than would those treated with furosemide. PATIENTS AND METHODS We conducted an open-label trial of 234 patients with chronic heart failure (mean [+/- SD] age, 64 +/- 11 years) from an urban public health care system. Patients received oral torsemide (n = 113) or furosemide (n = 121) for 1 year. The primary endpoint was readmission to the hospital for heart failure. Secondary endpoints included readmission for all cardiovascular causes and for all causes, numbers of hospital days, and health-related quality of life. RESULTS Compared with furosemide-treated patients, torsemide-treated patients were less likely to need readmission for heart failure (39 [32%] vs. 19 [17%], P <0.01) or for all cardiovascular causes (71 [59%] vs. 50 [44%], P = 0.03). There was no difference in the rate of admissions for all causes (92 [76%] vs. 80 [71%], P = 0.36). Patients treated with torsemide had significantly fewer hospital days for heart failure (106 vs. 296 days, P = 0.02). Improvements in dyspnea and fatigue scores from baseline were greater among patients treated with torsemide, but the differences were statistically significant only for fatigue scores at months 2, 8, and 12. CONCLUSIONS Compared with furosemide-treated patients, torsemide-treated patients were less likely to be readmitted for heart failure and for all cardiovascular causes, and were less fatigued. If our results are confirmed by blinded trials, torsemide may be the preferred loop diuretic for patients with chronic heart failure.

Computerized decision support to reduce potentially inappropriate prescribing to older emergency department patients: a randomized, controlled trial.

OBJECTIVES: To evaluate the effectiveness of computer‐assisted decision support in reducing potentially inappropriate prescribing to older adults.

A Framework for Capturing Clinical Data Sets from Computerized Sources

The pressure to improve health care and provide better care at a lower cost has created new needs to access clinical data for outcome analysis [1], quality assessment, guideline development [2], utilization review, pharmacoepidemiology [3], public health, benefits management, and other purposes. These needs are usually identified as data sets (that is, predefined lists of clinical questions or observations). Data sets are not new to the health care industry. The UB92 hospital billing form and UB82, its progenitor, from The Health Care Financing Administration (HCFA) have been around for some time. Recently, however, the number and richness of clinical data sets have grown dramatically. New data sets have been established by the National Center for Vital Health Statistics [4] and the National Committee for Quality Assurance [5]. The HCFA piloted an 1800-element quality-assurance data set called the Uniform Clinical Data Set System from 1989 to 1993 [6] and is working on a simpler version called the Medicare Quality Indicator System. Other HCFA data sets include the Resident Assessment Instrument for long-term health care [7] and a draft Outcome and Assessment Information Set for providers of home health care [8]. The U.S. Centers for Disease Control and Prevention (CDC) has developed Data Elements for Emergency Department Systems (DEEDS) for reporting information on visits to emergency departments [9]; the National Immunization Program for reporting data on immunizations [10]; and, in collaboration with the Council of State and Territorial Epidemiologists (CSTE) and the Association of State and Territorial Public Health Laboratory Directors (ASTPHLD), a data set that reports laboratory findings on communicable diseases [11]. Other national data sets include the Trauma Registry of American College of Surgeons [12], the Cardiovascular Data Standards for coronary arteriography [13], the Cooperative Project for coronary artery bypass graft surgery [14], and the Musculoskeletal Outcomes Data Evaluation and Management System for knee and hip replacements [15]. Cancer registries, hospitals, group practices, managed care providers, researchers, and pharmaceutical manufacturers are developing additional clinical data sets. We refer to the databases that carry data sets as analytic databases because they are usually designed for direct statistical analysis. As the need formal data sets has burgeoned, so has the use of computers to process patient information in direct support of patient care. Operational systems in the laboratory, pharmacy, patient registration area, surgical suites, and electrocardiography carts (to name a few) now include most data on laboratory procedures, prescriptions, demographics and appointments, surgical logs, and electrocardiographic measurements. Unfortunately, the two developments are occurring in independent orbits with little interaction. With a few important exceptions, developers of national data sets do not consider operational systems as sources for the contents of their data sets. Developers can find the information they want by abstracting charts. However, chart abstraction is prone to error and expensive. In one study, chart reviewers could not find 10% of the laboratory test results that were in the charts [16] and commercial chart reviews cost between

The IGNITE network: a model for genomic medicine implementation and research

10 and

Regulation of the branched-chain α-ketoacid dehydrogenase and elucidation of a molecular basis for maple syrup urine disease

15 per admission, depending on the amount of data retrieved (Kriss E. Personal communication. Boston, MA: MediQual). Chart reviews remain the only option for retrieving some kinds of information. However, when information exists in the databases of health care providers, manually extracting it from reports that are printed from one database and reentering the information into another database is time-consuming and inefficient. In this article, we review the barriers to the direct flow of operational data into analytic databases and the technical developments that have minimized these barriers. We also suggest specific actions that can unify the two orbits as the health care industry enters the computer age. The Difference between Operational and Analytic Databases Examples of operational databases are found in hospital pharmacies, laboratories, radiology departments, critical care units, and order-processing units. The first barriers to the direct use of operational system data in analytic databases are the differences in structure and detail that obscure similarities in the content of their information. A laboratory system would typically dedicate an entire record to each observation (for example, clinical measurement or laboratory test result). An ordering or pharmacy system would do the same for each item or prescription that is ordered. Table 1 shows the structure of an operational database for a clinical reporting system. Table 1. Operational Database: One Record per Observation* In contrast, analytic databases typically carry all variables of interest (for example, the most recent hemoglobin value, whether the patient is anemic, the number of units of blood transfused, and the lowest systolic blood pressure) in a single record that describes one patient, patient encounter, or patient procedure. Table 2 shows an analytic database analogue to the operational database of Table 1. In analytic databases, the variable is identified by the name of the field (for example, most recent cholesterol level) in which its value is stored, and all variables of interest are stored horizontally as separate fields in one record. The variables in an operational database are usually defined by a code or name stored in one field (with a name such as observation ID as shown in the third column of Table 1) and their values are stored in another field (with a name such as value as shown in the fourth column of Table 1). Different variables are stacked vertically in separate records. Table 2. Revised Model of an Analytic Database: One Record per Patient Event* Operational databases often contain repeated measurements (for example, all recent hemoglobin values for a patient), whereas analytic databases often contain a single measurement (for example, the lowest hemoglobin value during the first 24 hours of a hospital stay or the first Glasgow coma score during an emergency department visit). Operational databases usually carry many items of information about each value reported (for example, its units, date and time, and where the measurement was taken) as separate fields in the same record, whereas analytic databases usually contain only the variables value. However, analytic databases may contain slightly more information. For example, an analytic database may have the value and date of the last measurement of diastolic blood pressure. Operational databases usually contain raw data [for example, the hemoglobin value], whereas analytic databases frequently carry conclusions or yes or no answers to questions, such as is the patient anemic?). Finally, the identifying codes in operational databases tend to be more detailed than the corresponding codes in analytic databases. For example, an operational database in the pharmacy might identify a prescription by the National Drug Code (NDC), which identifies the brand name, dose, and bottle size. In comparison, the corresponding variable in an analytic database might identify drugs by a more generalized code that identifies only the generic drug (such as propranolol) or drug class (such as -blockers). In many cases, operational data can be converted into analytic variables. Three simple conversion rules are worth emphasizing. First, a continuous variable, such as the hemoglobin value or cholesterol level, can be converted into a binary diagnostic variable (such as specifying yes or no to the presence of anemia) and be given a numeric threshold that defines the diagnosis (for example, a hemoglobin value < 12). Second, detailed codes can be converted into more generalized codes by using simple cross-links (for example, converting NDC codes into generic drug codes). Finally, repeated values of a variable can be converted into a single value. Conversion occurs by selecting the first, last, or worst of a series of repeated values or by combining all occurrences on the basis of some rule. Examples include taking the mean value (as might be done for blood pressure levels), the sum (as might be done for determining chemotherapy drug doses), or the count (as might be done for records of blood transfusions). It is easy to imagine more complicated conversion rules. For example, a variable that specifies yes or no for the presence of diabetes might be defined in terms of thresholds on fasting blood sugar and hemoglobin A1c or for the current use of insulin or oral agents. Variations in the Codes and Structures of Operational Systems Until recently, a second barrier to the use of operational databases has been the lack of standards for reporting data from operational systems. Each vendor structured and reported the contents of its products differently. In some cases, each implementation of a vendors product also varied. In addition, each laboratory and medical records department tended to define its own unique and idiosyncratic codes for identifying observations and findings. This cacophony presented an enormous barrier to the use of operational databases by external agencies. Today, standard message structures and formats exist for exporting patient information from operational systems. Message standards specify a uniform structure for electronically reporting clinical data from source databases to other databases. These standards also specify the format for reporting dates, times, names, numeric values, and codes. For example, the standard for date formats is CCYYMMDD (century, year, month, date). Therefore, 12 April 1979 is recorded as 19790412 and not as 4-12-79, 12-apr-79, or any other option. The American National Standards Institute Health Level 7 (HL7) standard is the most relevant to this discussion. Th

An effective computerized reminder for contact isolation of patients colonized or infected with resistant organisms.

BackgroundPatients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility.MethodsTo address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches.ResultsThis paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years.ConclusionsThe IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these interventions. Through these efforts and collaboration with other stakeholders, IGNITE is poised to have a significant impact on the acceleration of genomic information into medical practice.

What is done, what is needed and what is realistic to expect from medical informatics standards

The hepatic branched-chain alpha-ketoacid dehydrogenase complex plays an important role in regulating branched-chain amino acid levels. These compounds are essential for protein synthesis but toxic if present in excess. When dietary protein is deficient, the hepatic enzyme is converted to the inactive, phosphorylated state to conserve branched-chain amino acids for protein synthesis. When dietary protein is excessive, the enzyme is in the active, dephosphorylated state to commit the excess branched-chain amino acids to degradation. Inhibition of protein synthesis by cycloheximide, even when the animal is starving for dietary protein, results in activation of the hepatic branched-chain alpha-ketoacid dehydrogenase complex to prevent accumulation of branched-chain amino acids. Likewise, the increase in branched-chain amino acids caused by body wasting during starvation and uncontrolled diabetes is blunted by activation of the hepatic branched-chain alpha-ketoacid dehydrogenase complex. The activity state of the complex is regulated in the short term by the concentration of branched-chain alpha-ketoacids (inhibitors of branched-chain alpha-ketoacid dehydrogenase kinase) and in the long term by alteration in total branched-chain alpha-ketoacid dehydrogenase kinase activity. cDNAs have been cloned and the primary structure of the mature proteins deduced for the E1 alpha subunit of the human and rat liver branched-chain alpha-ketoacid dehydrogenase complex. The cDNA and protein sequences are highly conserved for the two species. Considerable sequence similarity is also apparent between the E1 alpha subunits of the human branched-chain alpha-ketoacid dehydrogenase complex and the pyruvate dehydrogenase complex. Maple syrup urine disease is caused by an inherited deficiency in the branched-chain alpha-ketoacid dehydrogenase complex. The molecular basis of one maple syrup urine disease family has been determined for the first time. The patient was found to be a compound heterozygote, inheriting an allele encoding an abnormal E1 alpha from the father, and an allele which is not expressed from the mother. The only known animal model for the disease (Polled Hereford cattle) has also been characterized. The mutation in these animals introduces a stop codon in the leader peptide of the E1 alpha subunit, resulting in premature termination of translation. Two thiamine responsive patients have been studied. The deduced amino acid sequences of the mature E1 alpha subunit and its leader sequence were normal, suggesting that the defect in these patients must exist in some other subunit of the complex. 3-Hydroxyisobutyrate dehydrogenase and methylmalonate-semialdehyde dehydrogenase, two enzymes of the valine catabolic pathway, were purified from liver tissue and characterized.(ABSTRACT TRUNCATED AT 400 WORDS)

Computerized Decision Support for Medication Dosing in Renal Insufficiency: A Randomized, Controlled Trial

PURPOSE To improve contact isolation rates among patients admitted to the hospital with a known history of infection with Methicillin-resistant Staphylococcus aureus (MRSA) and Vancomycin-resistant Enterococci (VRE). METHODS A before and after interventional study implementing computerized reminders for contact isolation between February 25, 2005 and February 28, 2006. We measured rates of appropriate contact isolation, and time to isolation for the 4 month pre-intervention period, and the 12 month intervention period. We conducted a survey of ordering physicians at the midpoint of the intervention period. RESULTS Implementing a computerized reminder increased the rate of patients appropriately isolated from 33% to fully 89% (P<0.0001). The median time to writing contact isolation orders decreased from 16.6 to 0.0 h (P<0.0001). Physicians accepted the order 80% of the time on the first or second presentation. Ninety-five percent of physicians felt the reminder had no impact on workflow, or saved them time. CONCLUSION A human reviewed computerized reminder can achieve high rates of compliance with infection control recommendations for contact isolation, and dramatically reduce the time to orders being written upon admission.

Adherence to drug—drug interaction alerts in high-risk patients: a trial of context-enhanced alerting

Medical informatic experts have made considerable progress in the development of standards for orders and clinical results (CEN, HL7, ASTM), EKG tracings (CEN), diagnostic images (DICOM), claims processing (X12 and EDIFAC) and in vocabulary and codes (SNOMED, Read Codes, the MED, LOINC). Considerable work still remains to be carried out. Abstract models of health care information have to be created, to cover the necessary domain, and yet be simple enough to assimilate, implement, and manage. This requires a high degree of abstraction. Enormous amounts to develop standardized vocabulary are still required to complement such a model, and to define the subsets that apply to given contexts.