Initial clinical validation of Health Heritage, a patient-facing tool for personal and family history collection and cancer risk assessment.

Personal and family health histories remain important independent risk factors for cancer; however they are currently not being well collected or used effectively. Health Heritage was designed to address this need. The purpose of this study was to validate the ability of Health Heritage to identify patients appropriate for further genetic evaluation and to accurately stratify cancer risk. A retrospective chart review was conducted on 100 random patients seen at an adult genetics clinic presenting with concern for an inherited predisposition to cancer. Relevant personal and family history obtained from the patients' medical records was entered into Health Heritage. Recommendations by Health Heritage were compared to national guidelines of eligibility for genetic evaluation. Agreement between Health Heritage referral for genetic evaluation and guideline eligibility for genetic evaluation was 97% (sensitivity 98% and specificity 88%). Risk stratification for cancer was also compared between Health Heritage and those documented by a geneticist. For patients at increased risk for breast, ovarian, or colorectal cancer as determined by the geneticist, risk stratification by Health Heritage agreed 90, 93, and 75%, respectively. Discordances in risk stratification were attributed to both complex situations better handled by the geneticist and Health Heritage's adherence to incorporating all information into its algorithms. Health Heritage is a clinically valid tool to identify patients appropriate for further genetic evaluation and to encourage them to confirm the assessment and management recommendations with cancer genetic experts. Health Heritage also provides an estimate of cancer risk that is complementary to a genetics team.

Effect of pooled comparative information on judgments of quality.

Quality assessment is the focus of many health care initiatives. Yet it is not well understood how the type of information used in decision support tools to enable judgments of quality based on data impacts the accuracy, consistency and reliability of judgments made by physicians. Comparative pooled information could allow physicians to judge the quality of their practice by making comparisons to other practices or other specific populations of patients. In this study, resident physicians were provided with varying types of information derived from pooled patient data sets: quality component measures at the individual and group level, a qualitative interpretation of the quality measures using percentile rank, and an aggregate composite quality score. 32 participants viewed thirty quality profiles consisting of information applicable to the practice of thirty de-identified resident physicians. Those provided with quality component measures and a qualitative interpretation of the quality measures (rankings) judged quality of care more similarly to experts and were more internally consistent compared to participants who were provided with quality component measures alone. Reliability between participants was significantly less for those who were provided with a composite quality score compared to those who were not.

The Effect of Information Analysis Automation Display Content on Human Judgment Performance in Noisy Environments.

Displaying both the strategy that information analysis automation employs to makes its judgments and variability in the task environment may improve human judgment performance, especially in cases where this variability impacts the judgment performance of the information analysis automation. This work investigated the contribution of providing either information analysis automation strategy information, task environment information, or both, on human judgment performance in a domain where noisy sensor data are used by both the human and the information analysis automation to make judgments. In a simplified air traffic conflict prediction experiment, 32 participants made probability of horizontal conflict judgments under different display content conditions. After being exposed to the information analysis automation, judgment achievement significantly improved for all participants as compared to judgments without any of the automation's information. Participants provided with additional display content pertaining to cue variability in the task environment had significantly higher aided judgment achievement compared to those provided with only the automation's judgment of a probability of conflict. When designing information analysis automation for environments where the automation's judgment achievement is impacted by noisy environmental data, it may be beneficial to show additional task environment information to the human judge in order to improve judgment performance.

Psychophysical Detection of Inclusions with the Bare Finger amidst Softness Differentials.

Softness discrimination and the detection of inclusions are important in surgery and other medical tasks. To better understand how the characteristics of an inclusion (size, depth, hardness) and substrate (stiffness) affect their tactile detection and discrimination with the bare finger, we conducted a psychophysics experiment with eighteen participants. The results indicate that within a more pliant substrate (21 kPa), inclusions of 4 mm diameter (20 mm(3) volume) and greater were consistently detectable (above 75% of the time) but only at a depth of 5 mm. Inclusions embedded in stiffer substrates (82 kPa) had to be twice that volume (5 mm diameter, 40 mm(3) volume) to be detectable at the same rate. To analyze which tactile cues most impact stimulus detectability, we utilized logistic regression and generalized estimating equations. The results indicate that substrate stiffness most contributes to inclusion detectability, while the size, depth, and hardness of the stimulus follow in individual importance, respectively. The results seek to aid in the development of clinical tools and information displays and more accurate virtual haptic environments in discrimination of soft tissue.

Characterizing the range of simulated prostate abnormalities palpable by digital rectal examination.

BACKGROUND: Although the digital rectal exam (DRE) is a common method of screening for prostate cancer and other abnormalities, the limits of ability to perform this hands-on exam are unknown. Perceptible limits are a function of the size, depth, and hardness of abnormalities within a given prostate stiffness. METHODS: To better understand the perceptible limits of the DRE, we conducted a psychophysical study with 18 participants using a custom-built apparatus to simulate prostate tissue and abnormalities of varying size, depth, and hardness. Utilizing a modified version of the psychophysical method of constant stimuli, we uncovered thresholds of absolute detection and variance in ability between examiners. RESULTS: Within silicone-elastomers that mimic normal prostate tissue (21kPa), abnormalities of 4mm diameter (20mm(3) volume) and greater were consistently detectable (above 75% of the time) but only at a depth of 5mm. Abnormalities located in simulated tissue of greater stiffness (82kPa) had to be twice that volume (5mm diameter, 40mm(3) volume) to be detectable at the same rate. CONCLUSIONS: This study finds that the size and depth of abnormalities most influence detectability, while the relative stiffness between abnormalities and substrate also affects detectability for some size/depth combinations. While limits identified here are obtained for idealized substrates, this work is useful for informing the development of training and allowing clinicians to set expectations on performance.

SUPPORTING PHYSICIANS' PRACTICE-BASED LEARNING AND IMPROVEMENT (PBLI) AND QUALITY IMPROVEMENT THROUGH EXPLORATION OF POPULATION-BASED MEDICAL DATA.

Participating in self-assessment activities may stimulate improvement in practice behaviors. However, it is unclear how best to support the development of self-assessment skills, particularly in the health care domain. Exploration of population-based data is one method to enable health care providers to identify deficiencies in overall practice behavior that can motivate quality improvement initiatives. At the University of Virginia, we are developing a decision support tool to integrate and present population-based patient data to health care providers related to both clinical outcomes and non-clinical measures (e.g., demographic information). By enabling users to separate their direct impact on clinical outcomes from other factors out of their control, we may enhance the self-assessment process.