... Health innovations and point-of-care technologies conference. Health innovations and point-of-care technologies conference最新文献

Slowing cystic fibrosis (CF) lung disease progression is crucial to survival, but point-of-care technologies aimed at early detection-and possibly prevention-of rapid lung function decline are limited. This proof-of-principle study leverages a rich national patient registry and follow-up data on a local CF cohort to build an algorithm and prototype prognostic tool aimed at early detection of rapid lung function decline. The algorithm was developed using a novel longitudinal analysis of lung function (measured as forced expiratory volume in 1 s of % predicted, FEV1). Covariates included clinical and demographic characteristics selected from the registry based on information criterion. Preliminary assessment of algorithm performance suggested excellent predictive accuracy and earlier detection of rapid decline than standard of care being applied at a local center. Graphical displays were presented and evaluated for clinical utility. Predictions from the algorithms and chosen graphical displays were translated into a prototype web application using RShiny and underwent iterative development based on clinician feedback. This paper suggests that the algorithm and its translation could offer a means for earlier detection and treatment of rapid decline, providing clinicians with a viable point-of-care technology to intervene prior to irreversible lung damage.

Target controlled infusion (TCI) of intraveneous anesthetics can assist clinical practitioners to provide improved care for General Anesthesia (GA). Pharmacokinetic/Pharmacodynamic (PK/PD) models help in relating the anesthetic drug infusion to observed brain activity inferred from electroencephalogram (EEG) signals. The parameters in popular population PK/PD models for propofol-induced GA (Marsh and Schnider models) are either verified based on proprietary functions of the EEG signal which are difficult to correlate with the neurophysiological models of anesthesia, or the marker itself needs to be estimated simultaneously with the PD model. Both these factors make these existing paradigms challenging to apply in real-time context where a patient-specific tuning of parameters is desired. In this work, we propose a simpler EEG marker from frequency domain description of EEG and develop two corresponding PK/PD modeling approaches which differ in whether they use existing population-level PK models (approach 1) or not (approach 2). We use a simple deterministic parameter estimation approach to identify the unknown PK/PD model parameters from an existing human EEG data-set. We infer that both approaches 1 and 2 yield similar and reasonably good fits to the marker data. This work can be useful in developing patient-specific TCI strategies to induce GA.

In this paper, we present a Speech Enhancement (SE) method implemented on a smartphone, and this arrangement functions as an assistive device to hearing aids (HA). Many benchmark single channel SE algorithms implemented on HAs provide considerable improvement in speech quality, while speech intelligibility improvement still remains a prime challenge. The proposed SE method based on Log spectral amplitude estimator improves speech intelligibility in the noisy real world acoustic environment using the priori information of formant frequency locations. The formant frequency information avails us to control the amount of speech distortion in these frequency bands, thereby controlling speech distortion. We introduce a 'scaling' parameter for the SE gain function, which controls the gains over the non-formant frequency band, allowing the HA users to customize the playback speech using a smartphone application to their listening preference. Objective intelligibility measures show the effectiveness of the proposed SE method. Subjective results reflect the suitability of the developed Speech Enhancement application in real-world noisy conditions at SNR levels of -5 dB, 0 dB and 5 dB.

Even the most innovative healthcare technologies provide patient benefits only when adopted by clinicians and/or patients in actual practice. Yet realizing optimal positive impact from a new technology for the widest range of individuals who would benefit remains elusive. In software and new product development, iterative rapid-cycle "agile" methods more rapidly provide value, mitigate failure risks, and adapt to customer feedback. Co-development between builders and customers is a key agile principle. But how does one accomplish co-development with busy clinicians? In this paper, we discuss four practical agile co-development practices found helpful clinically: (1) User stories for lightweight requirements; (2) Time-boxed development for collaborative design and prompt course correction; (3) Automated acceptance test driven development, with clinician-vetted specifications; and (4) Monitoring of clinician interactions after release, for rapid-cycle product adaptation and evolution. In the coming wave of innovation in healthcare apps ushered in by open APIs to EHRs, learning rapidly what new product features work well for clinicians and patients will become even more crucial.

Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.

In this study, our collaborative research group explored the possibility of incorporating ultrasound elastography technology with a microfluidic device that is designed to prepare fine needle core biopsies (CBs; L=0.5-2.0 cm, D=0.4-1.2 mm) for pancreatic cancer diagnosis. For the first time, elastographic techniques were employed to measure shear wave velocity in fresh (3.7 m/s) and formalin-fixed (14.7 m/s) pancreatic CBs. Shear wave velocity did not vary whether fixed specimens were free on a microscope slide, or constrained within glass microfluidic channels: 11.5±1.9 v. 11.8±2.1 m/s. 4% agarose inclusions were also embedded within 1% agarose hydrogels to simulate cysts, neoplastic, or necrotic tissue within CBs. Inclusions were successfully visualized and measured using optical coherence elastography. These preliminary experiments demonstrate in a rudimentary fashion that elastographic measurements of pancreatic CBs may be incorporated with our microfluidic device. The rapid mapping of CB stiffness may provide qualitative spatial information for pathologists to determine a more accurate diagnosis for patients.

Sickle cell disease is a genetic mutation that causes sickling of the red blood cells, affecting between 90,000 and 100,000 Americans. Researchers must develop methods of data acquisition capable of maximizing both the amount of data being collected and types of data being collected to form the most accurate diagnosis and treatment for patients. Popular data acquisition forms are the use of mobile phones, sensory systems, and wearable technology. In this paper, we attempt to bridge the gap between the three, combining a wearable sensory system with the computation and communication power of mobile phones. We propose the application of sickle cell disease as a structure around which to design a textile-based data acquisition system.

Being able to perform a white blood cell (WBC) count and differential is a crucial laboratory test for basic diagnostic practices. In this paper, we demonstrate proof of concept results for a disposable cartridge that could be used to perform a WBC count and 3-part differential at the point-of-care. The cartridge is composed of a glass slide, a layer of transfer tape, and a glass cover slip and incorporates acridine orange for cell staining and sub-type differentiation; the stained blood is then imaged, and image analysis techniques return a WBC count and 3-part differential. The cartridge was tested on a laboratory microscope with 3 normal samples, and had promising results with 85.7% of images resulting in a WBC count with ±15% of the true value. Further, the 3-part differential concentrations determined using the disposable cartridge had strong correlations with the true concentrations (R² values of 0.9986, 0.9421, and 0.6942 for granulocytes, lymphocytes, and monocytes, respectively). Preliminary designs for a low-cost, portable microscope have been created and are currently being prototyped.

Anemia, a condition characterized by insufficient hemoglobin, affects 56.2% of pregnant women and 66.1% of children under five in low-resource countries. Though hemoglobin concentration measurement is the most common laboratory test in the world, the high cost of disposables (>$1.00 per test in Malawi) limits its availability in these settings. We have demonstrated a spectrophotometric method that reduces the per-test cost of anemia diagnosis to under $0.01 by using chromatography paper as the only disposable. Improvements in the hand-held reader, including using laser modules and a reference photodiode, enabled repeatable results within and across devices. We evaluated this method by analyzing capillary blood samples from 70 patients in the pediatric ward of Queen Elizabeth Central Hospital, Blantyre, Malawi. ~90% of these samples were within 2 g/dL of the standard value, with higher accuracy on more anemic samples. Current work aims to improve this accuracy by converting the hemoglobin in the sample to the more stable form methemoglobin.