Toward the development of national telehealth services: the role of Veterans Health Administration and future directions for research.

The Veterans Health Administration (VHA) in the Department of Veteran Affairs (VA) has emerged as a national and international leader in the delivery and research of telehealth-based treatment. Several unique characteristics of care in VA settings intersect to create an ideal environment for telehealth modalities and research. However, the value of telehealth experience and initiatives in VA settings is limited if telehealth strategies cannot be widely exported to other public or private systems. Whereas a hierarchical organization, such as VA, can innovate and fund change relatively quickly based on provider and patient preferences and a growing knowledge base, other health provider organizations and third-party payers may likely require replicable scientific findings over time before incremental investments will be made to create infrastructure, reform regulatory barriers, and amend laws to accommodate expansion of telehealth modalities. Accordingly, large-scale scientifically rigorous telehealth research in VHA settings is essential not only to investigate the efficacy of existing and future telehealth practices in VHA, but also to hasten the development of telehealth infrastructure in private and other public health settings. We propose an expanded partnership between the VA, NIH, and other funding agencies to investigate creative and pragmatic uses of telehealth technology. To this end, we identify six specific areas of research we believe to be particularly relevant to the efficient development of telehealth modalities in civilian and military contexts outside VHA.

Rationale and design of the Exercise Intensity Trial (EXCITE): A randomized trial comparing the effects of moderate versus moderate to high-intensity aerobic training in women with operable breast cancer.

BACKGROUND: The Exercise Intensity Trial (EXcITe) is a randomized trial to compare the efficacy of supervised moderate-intensity aerobic training to moderate to high-intensity aerobic training, relative to attention control, on aerobic capacity, physiologic mechanisms, patient-reported outcomes, and biomarkers in women with operable breast cancer following the completion of definitive adjuvant therapy. METHODS/DESIGN: Using a single-center, randomized design, 174 postmenopausal women (58 patients/study arm) with histologically confirmed, operable breast cancer presenting to Duke University Medical Center (DUMC) will be enrolled in this trial following completion of primary therapy (including surgery, radiation therapy, and chemotherapy). After baseline assessments, eligible participants will be randomized to one of two supervised aerobic training interventions (moderate-intensity or moderate/high-intensity aerobic training) or an attention-control group (progressive stretching). The aerobic training interventions will include 150 mins.wk⁻¹ of supervised treadmill walking per week at an intensity of 60%-70% (moderate-intensity) or 60% to 100% (moderate to high-intensity) of the individually determined peak oxygen consumption (VO₂peak) between 20-45 minutes/session for 16 weeks. The progressive stretching program will be consistent with the exercise interventions in terms of program length (16 weeks), social interaction (participants will receive one-on-one instruction), and duration (20-45 mins/session). The primary study endpoint is VO₂peak, as measured by an incremental cardiopulmonary exercise test. Secondary endpoints include physiologic determinants that govern VO₂peak, patient-reported outcomes, and biomarkers associated with breast cancer recurrence/mortality. All endpoints will be assessed at baseline and after the intervention (16 weeks). DISCUSSION: EXCITE is designed to investigate the intensity of aerobic training required to induce optimal improvements in VO₂peak and other pertinent outcomes in women who have completed definitive adjuvant therapy for operable breast cancer. Overall, this trial will inform and refine exercise guidelines to optimize recovery in breast and other cancer survivors following the completion of primary cytotoxic therapy. TRIAL REGISTRATION: NCT01186367.

Immunization with cocktail of HIV-derived peptides in montanide ISA-51 is immunogenic, but causes sterile abscesses and unacceptable reactogenicity.

BACKGROUND: A peptide vaccine was produced containing B and T cell epitopes from the V3 and C4 Envelope domains of 4 subtype B HIV-1 isolates (MN, RF, CanO, & Ev91). The peptide mixture was formulated as an emulsion in incomplete Freund's adjuvant (IFA). METHODS: Low-risk, healthy adult subjects were enrolled in a randomized, placebo-controlled dose-escalation study, and selected using criteria specifying that 50% in each study group would be HLA-B7+. Immunizations were scheduled at 0, 1, and 6 months using a total peptide dose of 1 or 4 mg. Adaptive immune responses in16 vaccine recipients and two placebo recipients after the 2nd immunization were evaluated using neutralization assays of sera, as well as ELISpot and ICS assays of cryopreserved PBMCs to assess CD4 and CD8 T-cell responses. In addition, (51)Cr release assays were performed on fresh PBMCs following 14-day stimulation with individual vaccine peptide antigens. RESULTS: 24 subjects were enrolled; 18 completed 2 injections. The study was prematurely terminated because 4 vaccinees developed prolonged pain and sterile abscess formation at the injection site-2 after dose 1, and 2 after dose 2. Two other subjects experienced severe systemic reactions consisting of headache, chills, nausea, and myalgia. Both reactions occurred after the second 4 mg dose. The immunogenicity assessments showed that 6/8 vaccinees at each dose level had detectable MN-specific neutralizing (NT) activity, and 2/7 HLA-B7+ vaccinees had classical CD8 CTL activity detected. However, using both ELISpot and ICS, 8/16 vaccinees (5/7 HLA-B7+) and 0/2 controls had detectable vaccine-specific CD8 T-cell responses. Subjects with moderate or severe systemic or local reactions tended to have more frequent T cell responses and higher antibody responses than those with mild or no reactions. CONCLUSIONS: The severity of local responses related to the formulation of these four peptides in IFA is clinically unacceptable for continued development. Both HIV-specific antibody and T cell responses were induced and the magnitude of response correlated with the severity of local and systemic reactions. If potent adjuvants are necessary for subunit vaccines to induce broad and durable immune responses, careful, incremental clinical evaluation is warranted to minimize the risk of adverse events. TRIAL REGISTRATION: ClinicalTrials.gov NCT00000886.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Cost-effectiveness analysis of the diagnosis of meniscus tears.

BACKGROUND: Diagnostic imaging represents the fastest growing segment of costs in the US health system. This study investigated the cost-effectiveness of alternative diagnostic approaches to meniscus tears of the knee, a highly prevalent disease that traditionally relies on MRI as part of the diagnostic strategy. PURPOSE: To identify the most efficient strategy for the diagnosis of meniscus tears. STUDY DESIGN: Economic and decision analysis; Level of evidence, 1. METHODS: A simple-decision model run as a cost-utility analysis was constructed to assess the value added by MRI in various combinations with patient history and physical examination (H&P). The model examined traumatic and degenerative tears in 2 distinct settings: primary care and orthopaedic sports medicine clinic. Strategies were compared using the incremental cost-effectiveness ratio (ICER). RESULTS: In both practice settings, H&P alone was widely preferred for degenerative meniscus tears. Performing MRI to confirm a positive H&P was preferred for traumatic tears in both practice settings, with a willingness to pay of less than US$50,000 per quality-adjusted life-year. Performing an MRI for all patients was not preferred in any reasonable clinical scenario. The prevalence of a meniscus tear in a clinician's patient population was influential. For traumatic tears, MRI to confirm a positive H&P was preferred when prevalence was less than 46.7%, with H&P preferred above that. For degenerative tears, H&P was preferred until the prevalence reaches 74.2%, and then MRI to confirm a negative was the preferred strategy. In both settings, MRI to confirm positive physical examination led to more than a 10-fold lower rate of unnecessary surgeries than did any other strategy, while MRI to confirm negative physical examination led to a 2.08 and 2.26 higher rate than H&P alone in primary care and orthopaedic clinics, respectively. CONCLUSION: For all practitioners, H&P is the preferred strategy for the suspected degenerative meniscus tear. An MRI to confirm a positive H&P is preferred for traumatic tears for all practitioners. Consideration should be given to implementing alternative diagnostic strategies as well as enhancing provider education in physical examination skills to improve the reliability of H&P as a diagnostic test. CLINICAL RELEVANCE: Alternative diagnostic strategies that do not include the use of MRI may result in decreased health care costs without harm to the patient and could possibly reduce unnecessary procedures.

Evaluating marker-guided treatment selection strategies

© 2014, The International Biometric Society.A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.

Practice-Level Variation in Outpatient Cardiac Care and Association With Outcomes.

BACKGROUND: Utilization of cardiac services varies across regions and hospitals, yet little is known regarding variation in the intensity of outpatient cardiac care across cardiology physician practices or the association with clinical endpoints, an area of potential importance to promote efficient care. METHODS AND RESULTS: We included 7 160 732 Medicare beneficiaries who received services from 5635 cardiology practices in 2012. Beneficiaries were assigned to practices providing the plurality of office visits, and practices were ranked and assigned to quartiles using the ratio of observed to predicted annual payments per beneficiary for common cardiac services (outpatient intensity index). The median (interquartile range) outpatient intensity index was 1.00 (0.81-1.24). Mean payments for beneficiaries attributed to practices in the highest (Q4) and lowest (Q1) quartile of outpatient intensity were: all cardiac payments (Q4 $1272 vs Q1 $581; ratio, 2.2); cardiac catheterization (Q4 $215 vs Q1 $64; ratio, 3.4); myocardial perfusion imaging (Q4 $253 vs Q1 $83; ratio, 3.0); and electrophysiology device procedures (Q4 $353 vs Q1 $142; ratio, 2.5). The adjusted odds ratios (95% CI) for 1 incremental quartile of outpatient intensity for each outcome was: cardiac surgical/procedural hospitalization (1.09 [1.09, 1.10]); cardiac medical hospitalization (1.00 [0.99, 1.00]); noncardiac hospitalization (0.99 [0.99, 0.99]); and death at 1 year (1.00 [0.99, 1.00]). CONCLUSION: Substantial variation in the intensity of outpatient care exists at the cardiology practice level, and higher intensity is not associated with reduced mortality or hospitalizations. Outpatient cardiac care is a potentially important target for efforts to improve efficiency in the Medicare population.