Improve Patient Monitoring Research Articles

Extending Patient-Chatbot Experience with Internet-of-Things and Background Knowledge: Case Studies with Healthcare Applications.

Presents case studies in the healthcare industry that focus on the use of Chatbots to improve patient monitoring and medical services. The transition towards personalized health management requires public awareness about management strategies of self-monitoring, self-appraisal, and self-management, eventually paving a way to more timely interventions and higher quality patient–clinician interactions. A key enabler is patient generated health data, fueled in good part by the growth in wearable devices including smart watches and other Internet-of- Things (IoT) for health-tracking. These tracking devices provide “low-level” monitoring signals indicating health conditions such as sleep apnea and heart rhythm disorder. However, to make more sense of IoT data, it is imperative that we develop cognitive approaches where they mine, interlink, and abstract diverse IoT data. These cognitive approaches often needs to keep the user closely engaged to acquire more information, to obtain feedback, to collect verbal health conditions, and to provide intervention and management actions. The chatbot technology was initially introduced as an artificial conversational agent to simulate conversations with a user using voice or text interactions.

Reconstruction of the 12‐lead ECG using a novel MR‐compatible ECG sensor network

Current electrocardiography (ECG) devices in MRI use non-conventional electrode placement, have a narrow bandwidth, and suffer from signal distortions including magnetohydrodynamic (MHD) effects and gradient-induced artifacts. In this work a system is proposed to obtain a high-quality 12-lead ECG. A network of N electrically independent MR-compatible ECG sensors was developed (N = 4 in this study). Each sensor uses a safe technology - short cables, preamplification/digitization close to the patient, and optical transmission - and provides three bipolar voltage leads. A matrix combination is applied to reconstruct a 12-lead ECG from the raw network signals. A subject-specific calibration is performed to identify the matrix coefficients, maximizing the similarity with a true 12-lead ECG, acquired with a conventional 12-lead device outside the scan room. The sensor network was subjected to radiofrequency heating phantom tests at 3T. It was then tested in four subjects, both at 1.5T and 3T. Radiofrequency heating at 3T was within the MR-compatibility standards. The reconstructed 12-lead ECG showed minimal MHD artifacts and its morphology compared well with that of the true 12-lead ECG, as measured by correlation coefficients above 93% (respectively, 84%) for the QRS complex shape during steady-state free precession (SSFP) imaging at 1.5T (respectively, 3T). High-quality 12-lead ECG can be reconstructed by the proposed sensor network at 1.5T and 3T with reduced MHD artifacts compared to previous systems. The system might help improve patient monitoring and triggering and might also be of interest for interventional MRI and advanced cardiac MR applications.

An intervention including the national early warning score improves patient monitoring practice and reduces mortality: A cluster randomized controlled trial.

To investigate the impact of the national early warning score on the frequency and the quality of vital sign registration and to study the association between protocol compliance and patient mortality. We conducted a post hoc data analysis of a stepped wedge cluster randomized controlled trial (RCT) in six hospitals. All adult, non-pregnant patients admitted to 24 wards were included. The intervention comprised an observation protocol using the national early warning score combined with a pragmatic medical response strategy. Data collection lasted from October 2013-May 2015. Patient comorbidity scores and vital signs were sampled every 4months on each ward. All vital signs in the 24hr before a serious adverse event were collected. Patients (N=60,956) were included of which 32,722 in the intervention group. Comorbidity scores were sampled in 3,600 patients and vital signs in 2,951 patients. In 668 patients, vital signs were collected before a serious adverse event. The mean number of vital signs per observation increased significantly in the intervention group. The observation frequency increased in patients with a serious adverse event and decreased in patients without a serious adverse event. Protocol compliance was negatively associated with patient mortality adjusted for comorbidity and age. Our intervention improved patient monitoring practice and reduced mortality. The impact of early warning scores on patient monitoring practice and patient outcomes remains unclear. Our intervention improved the observation of patients and reduced patient mortality. These results could support hospitals in their decision to implement rapid response systems. We have registered this study in the clinicaltrials.gov database (identifier: NCT01949025).

Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

Identifying patients at risk of not achieving meaningful gains in long-term postsurgical patient-reported outcome measures (PROMs) is important for improving patient monitoring and facilitating presurgical decision support. Machine learning may help automatically select and weigh many predictors to create models that maximize predictive power. However, these techniques are underused among studies of total joint arthroplasty (TJA) patients, particularly those exploring changes in postsurgical PROMs. QUESTION/PURPOSES: (1) To evaluate whether machine learning algorithms, applied to hospital registry data, could predict patients who would not achieve a minimally clinically important difference (MCID) in four PROMs 2 years after TJA; (2) to explore how predictive ability changes as more information is included in modeling; and (3) to identify which variables drive the predictive power of these models. Data from a single, high-volume institution's TJA registry were used for this study. We identified 7239 hip and 6480 knee TJAs between 2007 and 2012, which, for at least one PROM, patients had completed both baseline and 2-year followup surveys (among 19,187 TJAs in our registry and 43,313 total TJAs). In all, 12,203 registry TJAs had valid SF-36 physical component scores (PCS) and mental component scores (MCS) at baseline and 2 years; 7085 and 6205 had valid Hip and Knee Disability and Osteoarthritis Outcome Scores for joint replacement (HOOS JR and KOOS JR scores), respectively. Supervised machine learning refers to a class of algorithms that links a mapping of inputs to an output based on many input-output examples. We trained three of the most popular such algorithms (logistic least absolute shrinkage and selection operator (LASSO), random forest, and linear support vector machine) to predict 2-year postsurgical MCIDs. We incrementally considered predictors available at four time points: (1) before the decision to have surgery, (2) before surgery, (3) before discharge, and (4) immediately after discharge. We evaluated the performance of each model using area under the receiver operating characteristic (AUROC) statistics on a validation sample composed of a random 20% subsample of TJAs excluded from modeling. We also considered abbreviated models that only used baseline PROMs and procedure as predictors (to isolate their predictive power). We further directly evaluated which variables were ranked by each model as most predictive of 2-year MCIDs. The three machine learning algorithms performed in the poor-to-good range for predicting 2-year MCIDs, with AUROCs ranging from 0.60 to 0.89. They performed virtually identically for a given PROM and time point. AUROCs for the logistic LASSO models for predicting SF-36 PCS 2-year MCIDs at the four time points were: 0.69, 0.78, 0.78, and 0.78, respectively; for SF-36 MCS 2-year MCIDs, AUROCs were: 0.63, 0.89, 0.89, and 0.88; for HOOS JR 2-year MCIDs: 0.67, 0.78, 0.77, and 0.77; for KOOS JR 2-year MCIDs: 0.61, 0.75, 0.75, and 0.75. Before-surgery models performed in the fair-to-good range and consistently ranked the associated baseline PROM as among the most important predictors. Abbreviated LASSO models performed worse than the full before-surgery models, though they retained much of the predictive power of the full before-surgery models. Machine learning has the potential to improve clinical decision-making and patient care by helping to prioritize resources for postsurgical monitoring and informing presurgical discussions of likely outcomes of TJA. Applied to presurgical registry data, such models can predict, with fair-to-good ability, 2-year postsurgical MCIDs. Although we report all parameters of our best-performing models, they cannot simply be applied off-the-shelf without proper testing. Our analyses indicate that machine learning holds much promise for predicting orthopaedic outcomes. LEVEL OF EVIDENCE: Level III, diagnostic study.

Development and Deployment of an Open, Modular, Near-Real-Time Patient Monitor Datastream Conduit Toolkit to Enable Healthcare Multimodal Data Fusion in a Live Emergency Department Setting for Experimental Bedside Clinical Informatics Research

Healthcare providers rely on complex biomedical devices to assess, treat, and monitor patients. Ongoing research efforts are attempting to generate and implement better algorithms and mechanisms to ensure the early, accurate, automated, and clinically meaningful recognition of patterns and changes in patient health and pathology. Effecting such evolutionary advances in patient monitoring will likely require large collections of high-resolution physiologic parameter datasets from a broad spectrum of patients. As part of a research program to scientifically improve patient monitoring (with a focus on alarm fatigue mitigation), investigators developed the Medical Technology Interface-Open/Research toolkit with modular conduit components that provide the following capabilities: 1) access to select bedside monitor physiologic signals in real-world clinical settings for near-real-time acquisition, storage, and export of high-resolution patient datastreams in a portable format (.json); 2) establishment of a safe, parallel test environment at the bedside for experimental datastream analyses in a research framework. Deployment and interfacing of toolkit elements with off-the-shelf software solutions in a live emergency department setting enabled the construction of a bedside clinical informatics (BCI) research pipeline infrastructure that featured 1) indexing, search/query, and retrieval of datastreams for sophisticated analyses, experimental processing, and algorithm development; and 2) dataset visualization for expert adjudication of datastream interpretability, alarm clinical significance and severity, and experimental algorithm performance. In order to help institute a collaborative biomedical engineering research resource, this article shares details of the active ED BCI data pipeline and presents preliminary examples of ongoing multimodal data fusion applications.

3D Analysis of Cortical and Trabecular Bone From Hip DXA:Precision and Trend Assessment Interval in PostmenopausalWomen

The 3D distribution of the cortical and trabecular bone mass is a critical component in determining the resistance of a bone to fracture that is not assessed in standard dual-energy X-ray absorptiometry (DXA) exams. In this work, we assessed in vivo short-term precision of measurements provided by 3D modeling techniques from DXA scans and trend assessment intervals (TAIs) in postmenopausal women. Subjects included to study precision errors were scanned twice, with repositioning for duplicate hip scans, using either a Lunar iDXA scanner (GE Healthcare, Madison, WI) or a Discovery W scanner (Hologic, Inc., Waltham, MA). Postmenopausal women having baseline and 18-mo follow-up visit were scanned using a Lunar iDXA device to assess TAIs. TAIs indicate what time intervals are required to allow accurate assessment of response to treatment or progression of disease. The 3D-SHAPER software (Galgo Medical, Barcelona, Spain) was used to derive 3D measurements from hip DXA scans. Least significant changes were 10.39 and 8.72 mg/cm3 for integral volumetric bone mineral density (BMD), 9.64 and 9.59 mg/cm3 for trabecular volumetric BMD, and 6.25 and 5.99 mg/cm2 for cortical surface BMD, using the Lunar iDXA and Discovery W scanners, respectively. TAIs in postmenopausal women were 2.9 yr (integral volumetric BMD), 2.6 yr (trabecular volumetric BMD), and 3.5 yr (cortical surface BMD), using the Lunar iDXA scanner. As a comparison, TAIs for areal BMD were 2.8 yr at neck and 2.7 yr at total femur. Least significant changes of measurements provided by 3D modeling techniques from DXA were assessed. TAIs in postmenopausal women were similar to those measured for areal BMD measurements. DXA-derived 3D measurements could potentially provide additional indicators to improve patient monitoring in clinical practices.

From Registry Data to Real-Life Experiences: A Holistic Perspective of Psoriasis Treatment

Registries provide very high-quality data on the persistence of different therapies in the real world and can be used to compare and guide therapeutic guidelines. Dr Warren gave an overview of the different types of registries that capture data on patients with psoriasis. Furthermore, he discussed findings from the British Association of Dermatologists Biological Interventions Register (BADBIR), the Psoriasis Longitudinal Assessment and Registry (PSOLAR), and the Danish Biologic Interventions Registry (DERMBIO) data that help to gain insight on how best to prescribe drugs in a clinical setting. Prof Puig and Prof Gniadecki presented cases encountered in clinical practice to illustrate how real-world data can support the clinical decision-making process. Throughout their presentations, Prof Puig and Prof Gniadecki engaged the audience in interactive discussion on how to improve patient monitoring and management of comorbidities, and addressed issues such as drug survival, safety, and economics.

Improving detection of patient deterioration in the general hospital ward environment.

Patient monitoring on low acuity general hospital wards is currently based largely on intermittent observations and measurements of simple variables, such as blood pressure and temperature, by nursing staff. Often several hours can pass between such measurements and patient deterioration can go unnoticed. Moreover, the integration and interpretation of the information gleaned through these measurements remains highly dependent on clinical judgement. More intensive monitoring, which is commonly used in peri-operative and intensive care settings, is more likely to lead to the early identification of patients who are developing complications than is intermittent monitoring. Early identification can trigger appropriate management, thereby reducing the need for higher acuity care, reducing hospital lengths of stay and admission costs and even, at times, improving survival. However, this degree of monitoring has thus far been considered largely inappropriate for general hospital ward settings due to device costs and the need for staff expertise in data interpretation. In this review, we discuss some developing options to improve patient monitoring and thus detection of deterioration in low acuity general hospital wards.

A Simple Pre-Exposure Prophylaxis (PrEP) Optimization Intervention for Health Care Providers Prescribing PrEP: Pilot Study.

BackgroundPre-exposure prophylaxis (PrEP) has been shown to be highly effective for the prevention of HIV in clinical trials and demonstration projects, but PrEP uptake and adherence outside of these settings in the United States has been limited. Lack of knowledge and willingness of health care providers (HCPs) to prescribe PrEP is an important barrier to implementation.ObjectiveThe objective of this study was to describe and examine the feasibility and acceptability of a PrEP Optimization Intervention (PrEP-OI) targeted at HCPs. The ultimate purpose of this intervention was to increase PrEP uptake, adherence, and persistence among those at risk for HIV acquisition.MethodsThis intervention included the following: (1) a Web-based panel management tool called PrEP-Rx, which provides comprehensive HIV risk assessment, automates reminders for follow-up, and reports patients’ history of PrEP use; and (2) centralized PrEP coordination by a clinical support staff member (ie, the PrEP coordinator) who can identify individuals at risk for HIV, provide medical insurance navigation, and support multiple HCPs. Feasibility was evaluated based on HCPs’ ability to log in to PrEP-Rx and use it as needed. Acceptability was assessed via individual formative qualitative interviews with HCPs after 1 month of the intervention.ResultsThe intervention was feasible and acceptable among HCPs (N=6). HCPs identified system-level barriers to PrEP provision, many of which can be addressed by this intervention. HCPs noted that the intervention improved their PrEP knowledge; increased ease of PrEP prescription; and was likely to improve patient engagement and retention in care, enhance communication with patients, and improve patient monitoring and follow-up.ConclusionsGiven the critical role HCPs serve in disseminating PrEP, we created an easy-to-use PrEP optimization intervention deemed feasible and acceptable to providers. Further research on this tool and its ability to impact the PrEP continuum of care is needed.

B cells as biomarkers: predicting immune checkpoint therapy adverse events.

Immune checkpoint inhibitors are becoming a cornerstone of cancer immunotherapy as a result of their clinical success in relieving immune suppression and driving durable antitumor T cell responses in certain subsets of patients. Unfortunately, checkpoint inhibition is also associated with treatment-related toxicities that result in a myriad of side effects, ranging from mild and manageable to severe and debilitating. In this issue of the JCI, Das and colleagues report an association between early therapy-induced changes in circulating B cells and an increased risk of high-grade immune-related adverse events (IRAEs) in patients treated with checkpoint inhibitors that target cytotoxic T lymphocyte-associated antigen-4 (CTLA4) and programmed cell death protein 1 (PD1). These findings identify potential predictive biomarkers for high-grade IRAEs that may be leveraged to improve patient monitoring and may prompt new treatment strategies to prevent IRAEs.

Early-onset sepsis risk calculator reduces empiric antibiotic use

Kuzniewicz MW, Puopolo KM, Fischer A, Walsh EM, Li S, Newman TB, et al. A Quantitative, Risk-Based Approach to the Management of Neonatal Early-Onset Sepsis. JAMA Pediatr 2017;171:365-71. Question Among newborns, what is the clinical benefit of an early-onset sepsis (EOS) risk calculator, compared with national guidelines, in reducing antibiotic use? Design Cohort. Setting Kaiser Permanente Northern California (KPNC). Participants Neonates, 35 weeks of gestation or older. Intervention EOS calculator or national guidelines. Outcomes Empiric antibiotic use. Main Results Fewer babies in the EOS calculator group experienced blood culture use: adjusted number needed to treat (aNNT) 13 (95% CI, 8 to 42), and less empirical antibiotic administration in the first 24 hours: aNNT, 56 (95% CI, 44 to 77) with no difference between 24 and 72 hours after birth and no clinical outcome differences. Conclusions An EOS calculator reduced neonatal blood culture and empiric antibiotic use. Commentary A liberal threshold for initiating antibiotic therapy in neonates, based on early-onset sepsis (EOS) risk factors with poor predictive value, has led to overuse of antibiotics in non-infected neonates. This contrasts starkly with the low prevalence of EOS. Antibiotic overuse early in life may have severe short-term and long-term adverse consequences.1Esaiassen E. Fjalstad J.W. Juvet L.K. van den Anker J. Klingenberg C. Antibiotic exposure in neonates and early adverse outcomes — a systematic review and meta-analysis.J Antimicrob Chemother. 2017; 72: 1858-1870Crossref PubMed Scopus (83) Google Scholar, 2Bailey L.C. Forrest C.B. Zhang P. Richards T.M. Livshits A. DeRusso P.A. Association of antibiotics in infancy with early childhood obesity.JAMA Pediatr. 2014; 168: 1063-1069Crossref PubMed Scopus (329) Google Scholar This large cohort study from KPNC investigated clinical management of term and near-term infants with suspected or proven EOS over a 6-year period. The remarkable 50% relative reduction in antibiotic use after introduction of an EOS-calculator was not followed by a delay in therapy for infected infants or an increase in readmissions. It is evident that not only the EOS-calculator but also a bundle of co-interventions including improved patient monitoring was implemented, most likely at the same time. Moreover, the KPNC-staff must have undergone rigorous training in order to use the EOS-calculator and follow the bundle of interventions appropriately. The authors are to be congratulated for this very successful approach. The use of an EOS calculator has the potential to reduce antibiotic overuse, especially in areas where maternal risk factors, earlier, may have been over-emphasized. However, similar results may be achieved with different approaches. We recently presented data from a population-based study of 168,000 term infants in Norway over a 3-year period. In our setting, with a tradition of not recommending therapy based on risk factors alone, the proportion of term infants receiving antibiotic therapy in the first week of life was 2.3% and mortality was low.3Fjalstad J.W. Stensvold H.J. Bergseng H. Simonsen G.S. Salvesen B. Rønnestad A. et al.Early onset sepsis and antibiotic exposure in term infants: a nationwide population-based study in Norway.Pediatr Infect Dis J. 2016; 35: 1-6Crossref PubMed Scopus (1) Google Scholar

Paradoxical results obtained with Ki67-labeling and PHH3-mitosis index in glial tumors: a literature analysis.

Precise tiered tumor grading is essential for predicting prognosis, selecting different treatment options and for follow-up of brain tumor patients. Ki67 labeling index (LI) is widely employed in assessing aggressiveness of glial brain tumors. However, Ki67 is subject to interlaboratory variability, and its antigen is expressed on all cell cycle phases except G0, which hinders its usage as a precise criterion for assessing cell proliferation. Indeed, there exist peculiar observations pertinent to increases of Ki67 index in glial tumors following radiotherapy or treatment with tyrosine kinase inhibitors. Moreover, we have witnessed a reduced Ki67 labeling in a gemistocytic glioma during its rapid recurrence under temozolomide treatment. Therefore, we reviewed pitfalls in employing Ki67 indices for predicting glial tumor biology and tried to answer whether the mitotic marker PHH3 (phosphorylated histone H3) could provide additional information in predicting glial tumor biology. PHH3-based assessment of proliferating cell fraction provides novel potentials, but it has also its own weaknesses. It has not yet been determined whether it would be more advantageous to report: a mitotic count (MC) per unit-area (e.g., 10 high power fields (HPF)) or a mitotic index (MI) (per 1,000 tumor cells). Further, there exist peculiarities in terms of unexpectedly low or high PHH3 values in pilocytic astrocytomas and angiocentric gliomas, respectively. Indeed, we encountered almost no staining with PHH3 in our unique gemistocytic astrocytoma case. Hence, at least in some glial malignancies, PHH3 may not be necessary for cell proliferation. Awareness of the weaknesses of proliferation markers in brain tumors may improve patient monitoring and treatment. .

Fixed-Level Frequency Threshold Testing for Ototoxicity Monitoring.

Hearing loss from ototoxicity is often most pronounced at high frequencies. To improve patient monitoring and compliance, high-frequency testing methods should be short and easy to administer. We evaluated the repeatability and accuracy of a Békésy-like, fixed-level frequency threshold (FLFT) technique. This test takes less than a minute and could provide a rapid and effective way to determine the highest audible frequency. We hypothesized the FLFT test would be repeatable in normal-hearing subjects, and accurate when compared with Békésy fixed-frequency audiometry in the sensitive region for ototoxicity (SRO). Twenty-nine normal-hearing subjects (20 females, 9 males) performed 2 different automated audiometry tests at least 4 times over a period of no less than 3 weeks. Ages ranged from 23 to 35 years (average = 28 years). Subjects completed testing under Sennheiser HDA-200 headsets. Initial fixed-frequency audiometry thresholds were obtained at frequencies ranging from 0.5 to 20 kHz to identify each subject's highest audible frequency, which was used to determine the SRO. The SRO was defined as the seven frequencies at and below the highest audible frequency in 1/6-octave steps. These frequencies were monitored with fixed-frequency audiometry. At each session, the FLFT test was administered at 80 dB SPL. Subjects used a Békésy-style tracking method to determine the frequency threshold. All testing was completed in a sound booth (single wall, Industrial Acoustics Company) using a computerized, laptop-based, system. FLFT repeatability was calculated as the root mean square difference from the first test session. FLFT accuracy was calculated as the difference from the highest audible frequency determined from fixed-frequency audiometry interpolated to 80 dB SPL level. The FLFT average RMSD for intersession variability was 0.05 ± 0.05 octaves. The test showed no learning effect [F(3,78) = 0.7; p = 0.6]. The overall intersession variability for SRO fixed-frequency audiometry thresholds at all frequencies was within clinically acceptable test-retest variability (10 dB) at 5.8 dB (range 2.7 to 9.9 dB). The SRO fixed-frequency audiometry therefore served as a repeatable basis of comparison for accuracy of the FLFT test. The mean absolute difference between the fixed-frequency audiometry and FLFT-determined highest audible frequency was 0.03 octaves. The FLFT and the highest audible frequency via fixed-frequency audiometry at 80 dB SPL were not different statistically (p = 0.12). The FLFT took approximately 30 seconds to complete, compared with approximately 4.5 min for fixed-frequency audiometry SRO and 20 to 25 min for a traditional ototoxic audiometric assessment. The Békésy-style FLFT was repeatable within 1/12 octave (1 step size in the testing procedure). The FLFT agreed well with the highest audible frequency determined via fixed-frequency audiometry at 80 dB SPL. The FLFT test is amenable to automatic and self-administration and may enable quick, accurate, noise-tolerant ototoxicity, and high-frequency hearing monitoring.

Convenience without disclosure: a formative research study of a proposed integrated methadone and antiretroviral therapy service delivery model in Dar es Salaam, Tanzania

BackgroundThough timely initiation of antiretroviral therapy (ART) is a vital component of effective HIV prevention, care and treatment, people who inject drugs are less likely to receive ART than their non-drug using counterparts. In an effort to increase access to ART for people who inject drugs, we examined perceived benefits, challenges, and recommendations for implementing an integrated methadone and ART service delivery model at an opioid treatment program (OTP) clinic in Dar es Salaam, Tanzania.MethodsWe conducted in-depth interviews with 12 providers and 20 HIV-positive patients at the Muhimbili National Hospital OTP clinic in early 2015. We used thematic content analysis to examine patient and provider perspectives of an integrated model.ResultsRespondents perceived that offering on-site CD4 testing and HIV clinical management at the OTP clinic would improve the timeliness and efficiency of the ART eligibility process, make HIV clinical care more convenient, mitigate stigma and discrimination in HIV care and treatment settings, and improve patient monitoring and ART adherence. However, perceived challenges included overburdened OTP clinic staff and limited space at the clinic to accommodate additional services. Limited privacy at the OTP clinic and its contribution to fear among HIV-positive patients of being stigmatized by their peers at the clinic was a common theme expressed particularly by patients, and often corroborated by providers. Co-dispensing ART and methadone at the clinic’s pharmacy window was viewed as a potential deterrent for patients. Providers felt that an electronic health information system would help them better monitor patients’ progress, but that this system would need to be integrated into existing health information systems. To address these potential barriers to implementing an integrated model, respondents recommended increasing OTP provider and clinic capacity, offering flexible ART dispensing options, ensuring privacy with ART dispensing, and harmonizing any new electronic health information systems with existing systems.ConclusionsAn integrated methadone and ART service delivery model at the MNH OTP clinic could improve access to HIV care and treatment for OTP patients. However, specific implementation strategies must ensure that OTP providers are not overburdened and confidentiality of patients is maintained.

Reasons for missed appointments linked to a public-sector intervention targeting patients with stable chronic conditions in South Africa: results from in-depth interviews and a retrospective review of medical records

BackgroundMissed appointments serve as a key indicator for adherence to therapy and as such, identifying patient reasons for this inconsistency could assist in developing programmes to improve health outcomes. In this article, we explore the reasons for missed appointments linked to a centralised dispensing system in South Africa. This system dispenses pre-packed, patient-specific medication parcels for clinically stable patients to health facilities. However, at least 8%–12% of about 300,000 parcels are not collected each month. This article aims to establish whether missed appointments for collection of medicine parcels are indicative of loss-to-follow-up and also to characterise the patient and health system factors linked to missed appointments.MethodsWe applied an exploratory mixed-methods design in two overlapping research phases. This involved in-depth interviews to yield healthcare practitioners’ and patients’ experiences and medical record reviews. Data collection was conducted during the period 2014–2015. Qualitative data were analysed through a hybrid process of inductive and deductive thematic analysis which integrated data-driven and theory-driven codes. Data from medical records (N = 89) were analysed in MS excel using both descriptive statistics and textual descriptions.ResultsReview of medical records suggests that the majority of patients (67%) who missed original appointments later presented voluntarily to obtain medicines. This could indicate a temporal effect of some barriers. The remaining 33% revealed a range of CDU implementation issues resulting from, among others, erroneous classification of patients as defaulters. Interviews with patients revealed the following reasons for missed appointments: temporary migration, forgetting appointments, work commitments and temporary switch to private care. Most healthcare practitioners confirmed these barriers to collection but perceived that some were beyond the scope of health services. In addition, healthcare practitioners also identified a lack of patient responsibility, under-utilisation of medicines and use of plural healthcare sources (e.g. traditional healers) as contributing to missed appointments.ConclusionWe suggest developing a patient care model reflecting the local context, attention to improving CDU’s implementation processes and strengthening information systems in order to improve patient monitoring. This model presents lessons for other low-and-middle income countries with increasing need for dispensing of medicines for chronic illnesses.

Evaluation of hepatic fibrosis: a review from the society of abdominal radiology disease focus panel.

Hepatic fibrosis is potentially reversible; however early diagnosis is necessary for treatment in order to halt progression to cirrhosis and development of complications including portal hypertension and hepatocellular carcinoma. Morphologic signs of cirrhosis on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) alone are unreliable and are seen with more advanced disease. Newer imaging techniques to diagnose liver fibrosis are reliable and accurate, and include magnetic resonance elastography and US elastography (one-dimensional transient elastography and point shear wave elastography or acoustic radiation force impulse imaging). Research is ongoing with multiple other techniques for the noninvasive diagnosis of hepatic fibrosis, including MRI with diffusion-weighted imaging, hepatobiliary contrast enhancement, and perfusion; CT using perfusion, fractional extracellular space techniques, and dual-energy, contrast-enhanced US, texture analysis in multiple modalities, quantitative mapping, and direct molecular imaging probes. Efforts to advance the noninvasive imaging assessment of hepatic fibrosis will facilitate earlier diagnosis and improve patient monitoring with the goal of preventing the progression to cirrhosis and its complications.

Everybody working together

Wireless body area networks (WBANs) are rapidly growing in significance, partly due to their diverse and important applications. Perhaps the most important role they can play is in healthcare: from diagnosis, to monitoring and drug administration. Evidently, monitoring can save lives and a huge amount of research has been devoted to it but problems remain, especially in the context of sleep. The different nodes work cooperatively to improve patient monitoring during sleep As we will see below, sleeping patients are much more difficult to monitor reliably than patients who are awake, especially using WBANs. To address this, researchers from The Australian National University (ANU), and Australia's Information and Communications Technology Research Centre of Excellence (NICTA), have investigated various properties of WBAN channels to show that cooperative communication techniques can achieve a 20% performance improvement. WBANs represent the next generation of wireless personal area networks (WPANs). In this new generation, explained Samiya Shimly, one of the authors of the research, “miniaturised, intelligent, low power, invasive/non-invasive micro and nano technology sensor (and/or actuators) nodes are placed on, inside or around the human body for monitoring physiological signals.” Each WBAN then contains a hub which can connect to the internet or other relevant networks. Shimly told us that, while “WBAN offers a vast range of applications such as ubiquitous health care, military, sports, entertainment and many other areas, it is advanced healthcare that is the main focus of research.” The advantages of WBAN monitoring include patient quality of life – they can engage in their daily lives without being consigned indefinitely to hospital – as well as the ability to monitor several conditions at once. However, sleep still represents a problem, said Shimly, as “the sleeping body channel is a difficult channel to deal with: it is partially quasi-static in nature (not mobile, nor static), and it can be blocked for a long time due to changes in body posture, hence hindering reliability (when many patients can incur life-threatening risks during their sleep).” The work presented in this issue of Electronics Letters is the first to investigate the performance of sleeping body monitoring channels using different cooperative diversity techniques. The authors used cooperatively combined relayed links to improve significantly the reliability of monitoring a sleeping WBAN, which was not possible with single-hop communications. Expanding on this, Shimly explained that “two types of cooperative diversity techniques – 3-branch selection combining, and 3-branch switch-and-examine combining – are performed with two-hop relayed links, and then the results compared with single-hop communications.” This approach led to 7 dB and 20% performance improvement, in the cases of outage probability and outage duration, respectively. Significantly, she said, “the best-case outage probabilities estimated from single link and cooperatively combined links are not more than 10%, which indicates acceptable packet error rate within IEEE 802.15.6 standard requirements.” The improvements offered by the team's research mean that WBANs can be used to monitor patients continuously at home, in hospitals or in healthcare units. (Although, in the longer term, they also believe their prototype could be used for disaster response or emergency healthcare situations.) This will drastically improve the lives of patients in rehabilitation or those with disabilities. The associated devices are small and can activity in a number of situations However, the reliability of the monitoring process is just one part of the problem. The next steps the team are aiming to take are in patient mobility, security and reliability of data transfer, economy of power consumption and scalability. Shimly believes that if all of these aspects can be improved, then “WBAN technology will revolutionise the healthcare sector by enhancing health-care systems with the latest technological advancements leading to major life saving and reduction in healthcare costs.” She also expects that “WBANs wilI be pervasive across the society in many domains apart from healthcare, but it would be great to see WBANs fulfil their promise to save many lives, as well as delivering major improvements in quality of life.”

Health Resource Utilization and Cost for Patients with Incontinent Overactive Bladder Treated with Anticholinergics.

Overactive bladder (OAB) is a common medical condition with significant economic and humanistic burden. Inadequately managed OAB may exacerbate or result in comorbidities such as depression, falls, and urinary tract infections, which can further increase the burden to the health care system. Anticholinergics are often prescribed for management of OAB with urinary incontinence ("wet" OAB). However, research has shown that patient adherence and persistence to anticholinergic therapy is poor, with approximately 80% of patients ultimately failing their first prescribed anticholinergic medication within the first year. While there has been a fair amount of research on the economic burden of OAB, the real-world impact of initiating anticholinergic therapy in patients with wet OAB has not been well studied. To compare falls/fractures, anxiety/depression, health care resource utilization, and health care costs between a cohort of patients with wet OAB who initiated anticholinergic therapy and a matched cohort of patients without OAB. This study was a retrospective medical and pharmacy claims analysis. Cases were members of a primary care-based, multispecialty physician medical group located in California. Cases were eligible for inclusion if they were prescribed anticholinergic therapy between January 2008 and May 2012 based on pharmacy claims, had a diagnosis of OAB, and reported having ≥ 1 urinary incontinence episode per day. Wet OAB cases were matched to non-OAB controls in a 1:3 ratio based on sex, age, and observation time. Medical and pharmacy claims data were used to analyze patient comorbidities, as well as track health care resource utilization (HRU) and direct payer costs. After initiating anticholinergic therapy, wet OAB patients had a 46% higher adjusted risk of experiencing falls/fractures (P < 0.001) and a 33% higher adjusted risk of experiencing depression/anxiety (P = 0.022) than non-OAB patients. Wet OAB was significantly associated with increased HRU rates of hospital admissions, outpatient visits, prescriptions filled, and diagnostic tests performed. After adjustment for covariates, total health care cost was 33% higher for wet OAB patients than non-OAB patients, resulting in an increased cost of $1,746 per member per year. The findings of this research suggest OAB patients who initiate anticholinergic therapy and still experience incontinence are at a greater risk for comorbidities such as falls/fractures and depression/anxiety, and use significantly more health care resources, than patients without OAB. Programs to improve patient monitoring and referrals, the appropriate use of alternative treatments within guidelines, and adherence to evidence-based practice parameters may improve clinical outcomes and decrease HRU for these patients. This study was sponsored by Allergan, Irvine, California, which reviewed and approved the final manuscript. At the time of the study, Yehoshua had received a fellowship at the University of Arizona, which was funded by Allergan. Yehoshua, Joshi, and Campbell are employees of Allergan. Vasaveda has received consulting fees from Allergan, Medtronic, and Boston Scientific. Chancelor has received consulting fees from Allergan and Medtronic. All authors met the ICMJE authorship criteria. Neither honoraria nor payments were made for authorship. Study design was created by Yehoshua, Pulicharam, Malone, and Armstrong. Pulicharam took the lead in data collection, along with Chancellor and Campbell, and data interpretation was performed by Chancellor, Vasavada, Malone, and Armstrong. The manuscript was written by Yehoshua and revised by Joshi and Yehoshua, with assistance from the other authors.

Improving performance in the detection and management of cystic fibrosis-related diabetes in the Mountain West Cystic Fibrosis Consortium

ObjectiveCystic fibrosis (CF)-related diabetes (CFRD) is associated with increased morbidity and mortality. Improved detection and management may improve outcomes; however, actual practice falls short of published guidelines. We studied efforts...

Robust detection of heartbeats using association models from blood pressure and EEG signals.

BackgroundsThe heartbeat is fundamental cardiac activity which is straightforwardly detected with a variety of measurement techniques for analyzing physiological signals. Unfortunately, unexpected noise or contaminated signals can distort or cut out electrocardiogram (ECG) signals in practice, misleading the heartbeat detectors to report a false heart rate or suspend itself for a considerable length of time in the worst case. To deal with the problem of unreliable heartbeat detection, PhysioNet/CinC suggests a challenge in 2014 for developing robust heart beat detectors using multimodal signals.MethodsThis article proposes a multimodal data association method that supplements ECG as a primary input signal with blood pressure (BP) and electroencephalogram (EEG) as complementary input signals when input signals are unreliable. If the current signal quality index (SQI) qualifies ECG as a reliable input signal, our method applies QRS detection to ECG and reports heartbeats. Otherwise, the current SQI selects the best supplementary input signal between BP and EEG after evaluating the current SQI of BP. When BP is chosen as a supplementary input signal, our association model between ECG and BP enables us to compute their regular intervals, detect characteristics BP signals, and estimate the locations of the heartbeat. When both ECG and BP are not qualified, our fusion method resorts to the association model between ECG and EEG that allows us to apply an adaptive filter to ECG and EEG, extract the QRS candidates, and report heartbeats.ResultsThe proposed method achieved an overall score of 86.26 % for the test data when the input signals are unreliable. Our method outperformed the traditional method, which achieved 79.28 % using QRS detector and BP detector from PhysioNet. Our multimodal signal processing method outperforms the conventional unimodal method of taking ECG signals alone for both training and test data sets.Conclusions To detect the heartbeat robustly, we have proposed a novel multimodal data association method of supplementing ECG with a variety of physiological signals and accounting for the patient-specific lag between different pulsatile signals and ECG. Multimodal signal detectors and data-fusion approaches such as those proposed in this article can reduce false alarms and improve patient monitoring.