Explore the vast range of titles available.

Chronic pain is a widespread and complex set of conditions that are often difficult and expensive to treat. Comparative effectiveness research (CER) is an evolving research method that is useful in determining which treatments are most effective for medical conditions such as chronic pain. An underutilized mechanism for conducting CER in pain medicine involves combining patient-reported outcomes (PROs) with electronic health records (EHRs). Patient-reported pain and mental and physical health outcomes are increasingly collected during clinic visits, and these data can be linked to EHR data that are relevant to the treatment of a patient's pain, such as diagnoses, medications ordered, and medical comorbidities. When aggregated, this information forms a data repository that can be used for high-quality CER. This review provides a blueprint for conducting CER using PROs combined with EHRs. As an example, the University of Pittsburgh's patient outcomes repository for treatment is described. This system includes PROs collected via the Collaborative Health Outcomes Information Registry software and cross-linked data from the University of Pittsburgh Medical Center EHR. The requirements, best practice guidelines, statistical considerations, and caveats for performing CER with this type of data repository are also discussed.

Background There has been an increased significance on patient-reported outcomes in clinical settings. We aimed to evaluate the feasibility of administering patient-reported outcome measures by computerized adaptive testing (CAT) using a tablet computer with rehabilitation inpatients, assess workload demands on staff, and estimate the extent to which rehabilitation inpatients have elevated T-scores on six Patient Reported Outcomes Measurement Information System® (PROMIS®) measures. Methods Patients (N = 108) with stroke, spinal cord injury, traumatic brain injury, and other neurological disorders participated in this study. PROMIS computerized adaptive tests (CAT) were administered via a web-based platform. Summary scores were calculated for six measures: Pain Interference, Sleep Disruption, Anxiety, Depression, Illness Impact Positive, and Illness Impact Negative. We calculated the percent of patients with T-scores equivalent to 2 standard deviations or greater above the mean. Results During the first phase, we collected data from 19 of 49 patients; of the remainder, 61% were not available or had cognitive or expressive language impairments. In the second phase of the study, 40 of 59 patients participated to complete the assessment. The mean PROMIS T-scores were in the low 50 s, indicating an average symptom level, but 19–31% of patients had elevated T-scores where the patients needed clinical action. Conclusions The study demonstrated that PROMIS assessment using a CAT administration during an inpatient rehabilitation setting is feasible with the presence of a research staff member to complete PROMIS assessment.

Background: Measurement of headache impact is important in clinical trials, case detection, and the clinical monitoring of patients. Computerized adaptive testing (CAT) of headache impact has potential advantages over traditional fixed-length tests in terms of precision, relevance, real-time quality control and flexibility. Objective: To develop an item pool that can be used for a computerized adaptive test of headache impact. Methods: We analyzed responses to four well-known tests of headache impact from a population-based sample of recent headache sufferers (n = 1016). We used confirmatory factor analysis for categorical data and analyses based on item response theory (IRT). Results: In factor analyses, we found very high correlations between the factors hypothesized by the original test constructers, both within and between the original questionnaires. These results suggest that a single score of headache impact is sufficient. We established a pool of 47 items which fitted the generalized partial credit IRT model. By simulating a computerized adaptive health test we showed that an adaptive test of only five items had a very high concordance with the score based on all items and that different worst-case item selection scenarios did not lead to bias. Conclusion: We have established a headache impact item pool that can be used in CAT of headache impact.

Objective: To evaluate the feasibility of computerized adaptive testing (CAT) and the reliability and validity of CAT-based estimates of headache impact scores in comparison with 'static' surveys. Methods: Responses to the 54-item Headache Impact Test (HIT) were re-analyzed for recent headache sufferers (n = 1016) who completed telephone interviews during the National Survey of Headache Impact (NSHI). Item response theory (IRT) calibrations and the computerized dynamic health assessment (DYNHA®) software were used to simulate CAT assessments by selecting the most informative items for each person and estimating impact scores according to pre-set precision standards (CAT-HIT). Results were compared with IRT estimates based on all items (total-HIT), computerized 6-item dynamic estimates (CAT-HIT-6), and a developmental version of a 'static' 6-item form (HIT-6-D). Analyses focused on: respondent burden (survey length and administration time), score distributions ('ceiling' and 'floor' effects), reliability and standard errors, and clinical validity (diagnosis, level of severity). A random sample (n = 245) was re-assessed to test responsiveness. A second study (n = 1103) compared actual CAT surveys and an improved 'static' HIT-6 among current headache sufferers sampled on the Internet. Respondents completed measures from the first study and the generic SF-8™ Health Survey; some (n = 540) were re-tested on the Internet after 2 weeks. Results: In the first study, simulated CAT-HIT and total-HIT scores were highly correlated (r = 0.92) without 'ceiling' or 'floor' effects and with a substantial reduction (90.8%) in respondent burden. Six of the 54 items accounted for the great majority of item administrations (3603/5028, 77.6%). CAT-HIT reliability estimates were very high (0.975-0.992) in the range where 95% of respondents scored, and relative validity (RV) coefficients were high for diagnosis (RV = 0.87) and severity (RV = 0.89); patient-level classifications were accurate 91.3% for a diagnosis of migraine. For all three criteria of change, CAT-HIT scores were more responsive than all other measures. In the second study, estimates of respondent burden, item usage, reliability and clinical validity were replicated. The test-retest reliability of CAT-HIT was 0.79 and alternate forms coefficients ranged from 0.85 to 0.91. All correlations with the generic SF-8 were negative. Conclusions: CAT-based administrations of headache impact items achieved very large reductions in respondent burden without compromising validity for purposes of patient screening or monitoring changes in headache impact over time. IRT models and CAT-based dynamic health assessments warrant testing among patients with other conditions.

Interest in just-in-time adaptive interventions (JITAI) has rapidly increased in recent years. One core challenge for JITAI is the efficient and precise measurement of tailoring variables that are used to inform the timing of momentary intervention delivery. Ecological momentary assessment (EMA) is often used for this purpose, even though EMA in its traditional form was not designed specifically to facilitate momentary interventions. In this article, we introduce just-in-time adaptive EMA (JITA-EMA) as a strategy to reduce participant response burden and decrease measurement error when EMA is used as a tailoring variable in JITAI. JITA-EMA builds on computerized adaptive testing methods developed for purposes of classification (computerized classification testing, CCT), and applies them to the classification of momentary states within individuals. The goal of JITA-EMA is to administer a small and informative selection of EMA questions needed to accurately classify an individual’s current state at each measurement occasion. After illustrating the basic components of JITA-EMA (adaptively choosing the initial and subsequent items to administer, adaptively stopping item administration, accommodating dynamically tailored classification cutoffs), we present two simulation studies that explored the performance of JITA-EMA, using the example of momentary fatigue states. Compared with conventional EMA item selection methods that administered a fixed set of questions at each moment, JITA-EMA yielded more accurate momentary classification with fewer questions administered. Our results suggest that JITA-EMA has the potential to enhance some approaches to mobile health interventions by facilitating efficient and precise identification of momentary states that may inform intervention tailoring.

Patient-reported assessments are transforming many facets of health care, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden on both patients and health care professionals; improve test accuracy; and provide individualized, actionable feedback. The Concerto platform is a highly adaptable, secure, and easy-to-use console that can harness the power of CAT and machine learning for developing and administering advanced patient-reported assessments. This paper introduces readers to contemporary assessment techniques and the Concerto platform. It reviews advances in the field of patient-reported assessment that have been driven by the Concerto platform and explains how to create an advanced, adaptive assessment, for free, with minimal prior experience with CAT or programming.

Objectives The objectives of the present study are to investigate the precision of static (fixed-length) short forms versus computerized adaptive testing (CAT) administration, response pattern scoring versus summed score conversion, and test–retest reliability (stability) of the Patient-Reported Outcomes Measurement Information System (PROMIS®) pediatric self-report scales measuring the latent constructs of depressive symptoms, anxiety, anger, pain interference, peer relationships, fatigue, mobility, upper extremity functioning, and asthma impact with polytomous items. Methods Participants (N = 331) between the ages of 8 and 17 were recruited from outpatient general pediatrics and subspecialty clinics. Of the 331 participants, 137 were diagnosed with asthma. Three scores based on item response theory (IRT) were computed for each respondent: CAT response pattern expected a posteriori estimates, short-form response pattern expected a posteriori estimates, and short-form summed score expected a posteriori estimates. Scores were also compared between participants with and without asthma. To examine test–retest reliability, 54 children were selected for retesting approximately 2 weeks after the first assessment. Results A short CAT (maximum 12 items with a standard error of 0.4) was found, on average, to be less precise than the static short forms. The CAT appears to have limited usefulness over and above what can be accomplished with the existing static short forms (8–10 items). Stability of the scale scores over a 2-week period was generally supported. Conclusion The study provides further information on the psychometric properties of the PROMIS pediatric scales and extends the previous IRT analyses to include precision estimates of dynamic versus static administration, test–retest reliability, and validity of administration across groups. Both the positive and negative aspects of using CAT versus short forms are highlighted.

In the context of an ethnically diverse, aging society, attention is increasingly turning to health-related quality of life measurement to evaluate healthcare and treatment options for chronic diseases. When evaluating and treating symptoms and concerns such as fatigue, pain, or physical function, reliable and accurate assessment is a priority. Modern psychometric methods have enabled us to move from long, static tests that provide inefficient and often inaccurate assessment of individual patients, to computerized adaptive tests (CATs) that can precisely measure individuals on health domains of interest. These modern methods, collectively referred to as item response theory (IRT), can produce calibrated \"item banks\" from larger pools of questions. From these banks, CATs can be conducted on individuals to produce their scores on selected domains. Item banks allow for comparison of patients across different question sets because the patient's score is expressed on a common scale. Other advantages of using item banks include flexibility in terms of the degree of precision desired; interval measurement properties under most circumstances; realistic capability for accurate individual assessment over time (using CAT); and measurement equivalence across different patient populations. This work summarizes the process used in the creation and evaluation of item banks and reviews their potential contributions and limitations regarding outcome assessment and patient care, particularly when they are applied across people of different cultural backgrounds.

Computerized adaptive testing (CAT) aims to present items that statistically optimize the assessment process by considering the examinee’s responses and estimated trait levels. Recent developments in reinforcement learning and deep neural networks provide CAT with the potential to select items that utilize more information across all the items on the remaining tests, rather than just focusing on the next several items to be selected. In this study, we reformulate CAT under the reinforcement learning framework and propose a new item selection strategy based on the deep Q-network (DQN) method. Through simulated and empirical studies, we demonstrate how to monitor the training process to obtain the optimal Q-networks, and we compare the accuracy of the DQN-based item selection strategy with that of five traditional strategies—maximum Fisher information, Fisher information weighted by likelihood, Kullback‒Leibler information weighted by likelihood, maximum posterior weighted information, and maximum expected information—on both simulated and real item banks and responses. We further investigate how sample size and the distribution of the trait levels of the examinees used in training affect DQN performance. The results show that DQN achieves lower RMSE and MAE values than traditional strategies under simulated and real banks and responses in most conditions. Suggestions for the use of DQN-based strategies are provided, as well as their code.

The computerized adaptive form of cognitive diagnostic testing, CD-CAT, has gained increasing attention in the domain of personalized measurements for its ability to categorize individual mastery status of fine-grained attributes more accurately and efficiently through administering items tailored to one’s ability progressively. How to select the next item based on previous response(s) is crucial for the success of CD-CAT. Previous item selection strategies for CD-CAT have often followed a greedy or semi-greedy approach, which makes it difficult to strike a balance between diagnostic performance and item bank utilization. To address this issue, this study takes a graph perspective and transforms the item selection problem in CD-CAT into a path-searching problem, in which paths refer to possible test construction and nodes refer to individual items. A heuristic function is defined to predict the prospect of a path, indicating how well the corresponding test can diagnose the current examinee. Two search mechanisms with different biases towards item exposure control are proposed to approximate the optimal path with the best prospect. The first unused item on the resulting path is selected as the next item. The above components compose a novel CD-CAT item selection framework based on heuristic search. Simulation studies are conducted under a variety of conditions regarding bank designs, bank-quality conditions, and testing scenarios. The results are compared with different types of classic item selection strategies in CD-CAT, showing that the proposed framework can enhance bank utilization at a smaller cost of diagnostic performance.