David A. Hanauer

Computerized Automated Reminder Diabetes System (CARDS): E-Mail and SMS Cell Phone Text Messaging Reminders to Support Diabetes Management

BACKGROUND Cell phone text messaging, via the Short Messaging Service (SMS), offers the promise of a highly portable, well-accepted, and inexpensive modality for engaging youth and young adults in the management of their diabetes. This pilot and feasibility study compared two-way SMS cell phone messaging with e-mail reminders that were directed at encouraging blood glucose (BG) monitoring. METHODS Forty insulin-treated adolescents and young adults with diabetes were randomized to receive electronic reminders to check their BG levels via cell phone text messaging or e-mail reminders for a 3-month pilot study. Electronic messages were automatically generated, and participant replies with BG results were processed by the locally developed Computerized Automated Reminder Diabetes System (CARDS). Participants set their schedule for reminders on the secure CARDS website where they could also enter and review BG data. RESULTS Of the 40 participants, 22 were randomized to receive cell phone text message reminders and 18 to receive e-mail reminders; 18 in the cell phone group and 11 in the e-mail group used the system. Compared to the e-mail group, users in the cell phone group received more reminders (180.4 vs. 106.6 per user) and responded with BG results significantly more often (30.0 vs. 6.9 per user, P = 0.04). During the first month cell phone users submitted twice as many BGs as e-mail users (27.2 vs. 13.8 per user); by month 3, usage waned. CONCLUSIONS Cell phone text messaging to promote BG monitoring is a viable and acceptable option in adolescents and young adults with diabetes. However, maintaining interest levels for prolonged intervals remains a challenge.

New perspectives on the biology of acute GVHD

The use of allogeneic hematopoietic cell transplantation (HCT) has increased as new techniques have been developed for transplantation in patients who previously would not have been considered HCT candidates. However, its efficacy continued to be limited by the development of frequent and severe acute GVHD. The complex and intricate pathophysiology of acute GVHD is a consequence of interactions between the donor and host innate and adaptive immune responses. Multiple inflammatory molecules and cell types are implicated in the development of GVHD that can be categorized as: (1) triggers that initiate GVHD by therapy-induced tissue damage and the antigen disparities between host and graft tissue; (2) sensors that detect the triggers, that is, process and present alloantigens; (3) mediators such as T-cell subsets (naive, memory, regulatory, Th17 and natural killer T cells) and (4) the effectors and amplifiers that cause damage of the target organs. These multiple inflammatory molecules and cell types that are implicated in the development of GVHD have been described with models that use stepwise cascades. Herein, we provide a novel perspective on the immunobiology of acute GVHD and briefly discuss some of the outstanding questions and limitations of the model systems.

Elafin is a biomarker of graft-versus-host disease of the skin.

Plasma elafin concentrations correlate with graft-versus-host disease of the skin and long-term survival. Progress toward biomarker commercialization requires the discovery, qualification, verification, optimization, and clinical validation of a candidate before it is incorporated into existing therapeutic diagnostic platforms. The tremendous value that could be derived from the advancement of methods to detect disease at earlier and more treatable stages puts this pipeline approach at the forefront of biomarker development. However, to date there are no clear success stories in which discovery proteomics has led to a deployed protein biomarker. There is no polymerase chain reaction equivalent available to detect, quantify, and amplify proteins. Rather, proteomics-based biomarker discovery across a wide assortment of diseases is enabled by technologies such as mass spectrometry to sift through a large span of complex analytes at variable concentrations. Now, Paczesny and colleagues use a mass spectrometry–based technique to unambiguously identify candidate plasma biomarkers of skin acute graft-versus-host disease (GVHD)—the primary cause of nonrelapse mortality after bone marrow transplantation (BMT). Rashes are common after BMT and can be caused by a variety of reasons, but because the consequences of GVHD are serious, physicians initiate treatment of suspected GVHD without a bona fide confirmed diagnosis. In the discovery set of this work, the authors examined plasma samples from patients who had received BMT with and without clinical diagnosis of skin GVHD, and found that in patients with skin GVHD, the concentration of one lead marker, elafin, was three times as high. In a follow-up independent validation of 492 BMT patients, skin biopsies stained with elafin stratified the patients consistently according to GVHD parameters, and elafin plasma concentrations were concordantly higher in patients with GVHD. The specificity and sensitivity of elafin relative to other markers revealed that it was the single best discriminator for the diagnosis of GVHD in BMT patients with a rash, and was correlated with the severity of the disease. Elafin concentrations also correlated with the eventual maximum grade of GVHD and with nonrelapse mortality. These results show that elafin concentrations may serve as a noninvasive diagnostic test as well as a prognostic marker in determining GVHD grading in the clinic. Graft-versus-host disease (GVHD), the major complication of allogeneic bone marrow transplantation, affects the skin, liver, and gastrointestinal tract. There are no plasma biomarkers specific for any acute GVHD target organ. We used a large-scale quantitative proteomic discovery procedure to identify biomarker candidates of skin GVHD and validated the lead candidate, elafin, with enzyme-linked immunosorbent assay in samples from 492 patients. Elafin was overexpressed in GVHD skin biopsies. Plasma concentrations of elafin were significantly higher at the onset of skin GVHD, correlated with the eventual maximum grade of GVHD, and were associated with a greater risk of death relative to other known risk factors (hazard ratio, 1.78). We conclude that elafin has significant diagnostic and prognostic value as a biomarker of skin GVHD.

Public Awareness, Perception, and Use of Online Physician Rating Sites

Patients are increasingly turning to online physician ratings, just as they have sought ratings for other products and services. Much of what is known about these sites comes from studies of the ratings left on them.1 Little is known about the public’s awareness and use of online physician ratings, and whether these sites influence decisions about selecting a physician.

The MITRE Identification Scrubber Toolkit: Design, training, and assessment

PURPOSE Medical records must often be stripped of patient identifiers, or de-identified, before being shared. De-identification by humans is time-consuming, and existing software is limited in its generality. The open source MITRE Identification Scrubber Toolkit (MIST) provides an environment to support rapid tailoring of automated de-identification to different document types, using automatically learned classifiers to de-identify and protect sensitive information. METHODS MIST was evaluated with four classes of patient records from the Vanderbilt University Medical Center: discharge summaries, laboratory reports, letters, and order summaries. We trained and tested MIST on each class of record separately, as well as on pooled sets of records. We measured precision, recall, F-measure and accuracy at the word level for the detection of patient identifiers as designated by the HIPAA Safe Harbor Rule. RESULTS MIST was applied to medical records that differed in the amounts and types of protected health information (PHI): lab reports contained only two types of PHI (dates, names) compared to discharge summaries, which were much richer. Performance of the de-identification tool depended on record class; F-measure results were 0.996 for order summaries, 0.996 for discharge summaries, 0.943 for letters and 0.934 for laboratory reports. Experiments suggest the tool requires several hundred training exemplars to reach an F-measure of at least 0.9. CONCLUSIONS The MIST toolkit makes possible the rapid tailoring of automated de-identification to particular document types and supports the transition of the de-identification software to medical end users, avoiding the need for developers to have access to original medical records. We are making the MIST toolkit available under an open source license to encourage its application to diverse data sets at multiple institutions.

Implementation of the Quality Oncology Practice Initiative at a University Comprehensive Cancer Center

PURPOSE The Quality Oncology Practice Initiative (QOPI) is a voluntary program developed by the American Society of Clinical Oncology (ASCO) to aid oncology practices in quality self-assessment. Few academic cancer centers have been QOPI participants. METHODS We implemented the QOPI process at the University of Michigan Comprehensive Cancer Center, a large, hospital-based academic cancer center, and report our experience with five rounds of data collection. Patient medical records were selected using QOPI-specified procedures and abstracted locally; results were entered into an ASCO-maintained database and analyzed. RESULTS Abstractors who were not directly involved with patient care required an average of 62.3 minutes per medical record (4.7 minutes per data element) to abstract data. We found that compliance with quality measures was uniformly high when measures were structured into our electronic medical record. Results from other measures, including those measuring chemotherapy administration in the last 2 weeks of life, were initially markedly different from those reported by other QOPI participants. Our practice changed toward the QOPI national practice norm after a presentation of the results at a faculty research conference. We found that other measures were consistently greater than 90%, including disease-specific diagnosis and treatment measures. CONCLUSION Measuring and showing performance data to physicians was sufficient to change some aspects of physician behavior. Improvement in other measures requires structural practice changes. QOPI, an oncologist-developed system, can be adapted for use in practice improvement at an academic medical center.

Enhanced identification of eligibility for depression research using an electronic medical record search engine

PURPOSE Electronic medical records (EMRs) have become part of daily practice for many physicians. Attempts have been made to apply electronic search engine technology to speed EMR review. This was a prospective, observational study to compare the speed and clinical accuracy of a medical record search engine vs. manual review of the EMR. METHODS Three raters reviewed 49 cases in the EMR to screen for eligibility in a depression study using the electronic medical record search engine (EMERSE). One week later raters received a scrambled set of the same patients including 9 distractor cases, and used manual EMR review to determine eligibility. For both methods, accuracy was assessed for the original 49 cases by comparison with a gold standard rater. RESULTS Use of EMERSE resulted in considerable time savings; chart reviews using EMERSE were significantly faster than traditional manual review (p=0.03). The percent agreement of raters with the gold standard (e.g. concurrent validity) using either EMERSE or manual review was not significantly different. CONCLUSIONS Using a search engine optimized for finding clinical information in the free-text sections of the EMR can provide significant time savings while preserving clinical accuracy. The major power of this search engine is not from a more advanced and sophisticated search algorithm, but rather from a user interface designed explicitly to help users search the entire medical record in a way that protects health information.

Bioinformatics approaches in the study of cancer.

A revolution is underway in the approach to studying the genetic basis of cancer. Massive amounts of data are now being generated via high-throughput techniques such as DNA microarray technology and new computational algorithms have been developed to aid in analysis. At the same time, standards-based repositories, including the Stanford Microarray Database and the Gene Expression Omnibus have been developed to store and disseminate the results of microarray experiments. Bioinformatics, the convergence of biology, information science, and computation, has played a key role in these developments. Recently developed techniques include Module Maps, SLAMS (Stepwise Linkage Analysis of Microarray Signatures), and COPA (Cancer Outlier Profile Analysis). What these techniques have in common is the application of novel algorithms to find high-level gene expression patterns across heterogeneous microarray experiments. Large-scale initiatives are underway as well. The Cancer Genome Atlas (TCGA) project is a logical extension of the Human Genome Project and is meant to produce a comprehensive atlas of genetic changes associated with cancer. The Cancer Biomedical Informatics Grid (caBIG), led by the NCI, also represents a colossal initiative involving virtually all aspects of cancer research and may help to transform the way cancer research is conducted and data are shared.

Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

BACKGROUND Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. OBJECTIVE To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows. METHODS Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost. RESULTS From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods. CONCLUSIONS This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patients show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.

Exploring clinical associations using '-omics' based enrichment analyses.

Background The vast amounts of clinical data collected in electronic health records (EHR) is analogous to the data explosion from the “-omics” revolution. In the EHR clinicians often maintain patient-specific problem summary lists which are used to provide a concise overview of significant medical diagnoses. We hypothesized that by tapping into the collective wisdom generated by hundreds of physicians entering problems into the EHR we could detect significant associations among diagnoses that are not described in the literature. Methodology/Principal Findings We employed an analytic approach original developed for detecting associations between sets of gene expression data, called Molecular Concept Map (MCM), to find significant associations among the 1.5 million clinical problem summary list entries in 327,000 patients from our institutions EHR. An odds ratio (OR) and p-value was calculated for each association. A subset of the 750,000 associations found were explored using the MCM tool. Expected associations were confirmed and recently reported but poorly known associations were uncovered. Novel associations which may warrant further exploration were also found. Examples of expected associations included non-insulin dependent diabetes mellitus and various diagnoses such as retinopathy, hypertension, and coronary artery disease. A recently reported association included irritable bowel and vulvodynia (OR 2.9, p = 5.6×10−4). Associations that are currently unknown or very poorly known included those between granuloma annulare and osteoarthritis (OR 4.3, p = 1.1×10−4) and pyloric stenosis and ventricular septal defect (OR 12.1, p = 2.0×10−3). Conclusions/Significance Computer programs developed for analyses of “-omic” data can be successfully applied to the area of clinical medicine. The results of the analysis may be useful for hypothesis generation as well as supporting clinical care by reminding clinicians of likely problems associated with a patients existing problems.