Albert M. Lai

A Randomized Trial Comparing Telemedicine Case Management with Usual Care in Older, Ethnically Diverse, Medically Underserved Patients with Diabetes Mellitus: 5 Year Results of the IDEATel Study

CONTEXT Telemedicine is a promising but largely unproven technology for providing case management services to patients with chronic conditions and lower access to care. OBJECTIVES To examine the effectiveness of a telemedicine intervention to achieve clinical management goals in older, ethnically diverse, medically underserved patients with diabetes. DESIGN, Setting, and Patients A randomized controlled trial was conducted, comparing telemedicine case management to usual care, with blinded outcome evaluation, in 1,665 Medicare recipients with diabetes, aged >/= 55 years, residing in federally designated medically underserved areas of New York State. Interventions Home telemedicine unit with nurse case management versus usual care. Main Outcome Measures The primary endpoints assessed over 5 years of follow-up were hemoglobin A1c (HgbA1c), low density lipoprotein (LDL) cholesterol, and blood pressure levels. RESULTS Intention-to-treat mixed models showed that telemedicine achieved net overall reductions over five years of follow-up in the primary endpoints (HgbA1c, p = 0.001; LDL, p < 0.001; systolic and diastolic blood pressure, p = 0.024; p < 0.001). Estimated differences (95% CI) in year 5 were 0.29 (0.12, 0.46)% for HgbA1c, 3.84 (-0.08, 7.77) mg/dL for LDL cholesterol, and 4.32 (1.93, 6.72) mm Hg for systolic and 2.64 (1.53, 3.74) mm Hg for diastolic blood pressure. There were 176 deaths in the intervention group and 169 in the usual care group (hazard ratio 1.01 [0.82, 1.24]). CONCLUSIONS Telemedicine case management resulted in net improvements in HgbA1c, LDL-cholesterol and blood pressure levels over 5 years in medically underserved Medicare beneficiaries. Mortality was not different between the groups, although power was limited. Trial Registration http://clinicaltrials.gov Identifier: NCT00271739.

A review of approaches to identifying patient phenotype cohorts using electronic health records

Objective To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. Materials and methods We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. Results Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. Discussion We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. Conclusions There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

Limits of wide-area thin-client computing

While many application service providers have proposed using thin-client computing to deliver computational services over the Internet, little work has been done to evaluate the effectiveness of thin-client computing in a wide-area network. To assess the potential of thin-client computing in the context of future commodity high-bandwidth Internet access, we have used a novel, non-invasive slow-motion benchmarking technique to evaluate the performance of several popular thin-client computing platforms in delivering computational services cross-country over Internet2. Our results show that using thin-client computing in a wide-area network environment can deliver acceptable performance over Internet2, even when client and server are located thousands of miles apart on opposite ends of the country. However, performance varies widely among thin-client platforms and not all platforms are suitable for this environment. While many thin-client systems are touted as being bandwidth efficient, we show that network latency is often the key factor in limiting wide-area thin-client performance. Furthermore, we show that the same techniques used to improve bandwidth efficiency often result in worse overall performance in wide-area networks. We characterize and analyze the different design choices in the various thin-client platforms and explain which of these choices should be selected for supporting wide-area computing services.

Syndromic Surveillance Using Ambulatory Electronic Health Records

OBJECTIVE To assess the performance of electronic health record data for syndromic surveillance and to assess the feasibility of broadly distributed surveillance. DESIGN Two systems were developed to identify influenza-like illness and gastrointestinal infectious disease in ambulatory electronic health record data from a network of community health centers. The first system used queries on structured data and was designed for this specific electronic health record. The second used natural language processing of narrative data, but its queries were developed independently from this health record. Both were compared to influenza isolates and to a verified emergency department chief complaint surveillance system. MEASUREMENTS Lagged cross-correlation and graphs of the three time series. RESULTS For influenza-like illness, both the structured and narrative data correlated well with the influenza isolates and with the emergency department data, achieving cross-correlations of 0.89 (structured) and 0.84 (narrative) for isolates and 0.93 and 0.89 for emergency department data, and having similar peaks during influenza season. For gastrointestinal infectious disease, the structured data correlated fairly well with the emergency department data (0.81) with a similar peak, but the narrative data correlated less well (0.47). CONCLUSIONS It is feasible to use electronic health records for syndromic surveillance. The structured data performed best but required knowledge engineering to match the health record data to the queries. The narrative data illustrated the potential performance of a broadly disseminated system and achieved mixed results.

Repurposing the Clinical Record: Can an Existing Natural Language Processing System De-identify Clinical Notes?

Electronic clinical documentation can be useful for activities such as public health surveillance, quality improvement, and research, but existing methods of de-identification may not provide sufficient protection of patient data. The general-purpose natural language processor MedLEE retains medical concepts while excluding the remaining text so, in addition to processing text into structured data, it may be able provide a secondary benefit of de-identification. Without modifying the system, the authors tested the ability of MedLEE to remove protected health information (PHI) by comparing 100 outpatient clinical notes with the corresponding XML-tagged output. Of 809 instances of PHI, 26 (3.2%) were detected in output as a result of processing and identification errors. However, PHI in the output was highly transformed, much appearing as normalized terms for medical concepts, potentially making re-identification more difficult. The MedLEE processor may be a good enhancement to other de-identification systems, both removing PHI and providing coded data from clinical text.

Evaluation of a remote training approach for teaching seniors to use a telehealth system

OBJECTIVE There has been a growth of home healthcare technology in rural areas. However, a significant limitation has been the need for costly and repetitive training in order for patients to efficiently use their home telemedicine unit (HTU). This research describes the evaluation of an architecture for remote training of patients in a telemedicine environment. This work examines the viability of a remote training architecture called REmote Patient Education in a Telemedicine Environment (REPETE). REPETE was implemented and evaluated in the context of the IDEATel project, a large-scale telemedicine project, focusing on Medicare beneficiaries with diabetes in New York State. METHODS A number of qualitative and quantitative evaluation tools were developed and used to study the effectiveness of the remote training sessions evaluating: (a) task complexity, (b) changes in patient performance and (c) the communication between trainer and patient. Specifically, the effectiveness of the training was evaluated using a measure of web skills competency, a user satisfaction survey, a cognitive task analysis and an interaction analysis. RESULTS Patients not only reported that the training was beneficial, but also showed significant improvements in their ability to effectively perform tasks. Our qualitative evaluations scrutinizing the interaction between the trainer and patient showed that while there was a learning curve for both the patient and trainer when negotiating the shared workspace, the mutually visible pointer used in REPETE enhanced the computer-mediated instruction. CONCLUSIONS REPETE is an effective remote training tool for older adults in the telemedicine environment. Patients demonstrated significant improvements in their ability to perform tasks on their home telemedicine unit.

Improving web browsing performance on wireless pdas using thin-client computing

Web applications are becoming increasingly popular for mobile wireless PDAs. However, web browsing on these systems can be quite slow. An alternative approach is handheld thin-client computing, in which the web browser and associated application logic run on a server, which then sends simple screen updates to thePDA for display. To assess the viability of this thin-client approach, we compare the web browsing performance of thin clients against fat clients that run the web browser locally on a PDA. Our results show that thin clients can provide better web browsing performance compared to fat clients, both in terms of speed and ability to correctly display web content. Surprisingly, thin clients are faster even when having to send more data over the network. We characterize and analyze different design choices in various thin-client systems and explain why these approaches can yield superior web browsing performance on mobile wireless PDAs.

Stochastic curtailment of health questionnaires: A method to reduce respondent burden

Stochastic curtailment is a sequential method to terminate a study when continuing to the end would be unlikely to change the outcome. This method has been researched most commonly in the context of clinical trials. The current paper explores its use in a different setting: the administration of a health questionnaire to patients via computer. A classification procedure augmenting logistic regression with stochastic curtailment is introduced to avoid burdening the patients with unnecessary questions. In a real-data simulation using responses from the Medicare Health Outcomes Survey, the new procedure substantially reduced the average number of questions administered with a minimal loss of classification accuracy.

Assessment of Life's Simple 7 in the primary care setting: the Stroke Prevention in Healthcare Delivery EnviRonmEnts (SPHERE) study.

BACKGROUND Adverse health behaviors and factors predict increased coronary heart disease and stroke risk, and effective use of health information technology (HIT) to automate assessment of and intervention on these factors is needed. A comprehensive, automated cardiovascular health (CVH) assessment deployed in the primary care setting offers the potential to enhance prevention, facilitate patient-provider communication, and ultimately reduce cardiovascular (CV) disease risk. We describe the methods for a study to develop and test an automated CVH application for stroke prevention in older women. METHODS AND RESULTS The eligible study population for the Stroke Prevention in Healthcare Delivery EnviRonmEnts (SPHERE) study is approximately 1600 female patients aged 65 years and older and their primary care providers at The Ohio State University Wexner Medical Center. We will use an intervention design that will allow for a run-in period, comparison group data collection, a provider education period, and implementation of a best practice alert to prompt provider-patient interactions regarding CVH. Our primary outcome is a CVH score, comprising Lifes Simple 7: smoking status, body mass index, blood pressure, cholesterol, fasting glucose, physical activity, and diet. The SPHERE application will generate visualizations of the CVH score within the electronic health record (EHR) during the patient-provider encounter. A key outcome of the study will be change in mean CVH score pre- and post-intervention. CONCLUSIONS The SPHERE application leverages the EHR and may improve health outcomes through HIT designed to empower clinicians to discuss CVH with their patients and enhance primary prevention efforts.

Comparison of UMLS terminologies to identify risk of heart disease using clinical notes

The second track of the 2014 i2b2 challenge asked participants to automatically identify risk factors for heart disease among diabetic patients using natural language processing techniques for clinical notes. This paper describes a rule-based system developed using a combination of regular expressions, concepts from the Unified Medical Language System (UMLS), and freely-available resources from the community. With a performance (F1=90.7) that is significantly higher than the median (F1=87.20) and close to the top performing system (F1=92.8), it was the best rule-based system of all the submissions in the challenge. We also used this system to evaluate the utility of different terminologies in the UMLS towards the challenge task. Of the 155 terminologies in the UMLS, 129 (76.78%) have no representation in the corpus. The Consumer Health Vocabulary had very good coverage of relevant concepts and was the most useful terminology for the challenge task. While segmenting notes into sections and lists has a significant impact on the performance, identifying negations and experiencer of the medical event results in negligible gain.