Patient Self-Monitoring Using Intelligent Phonocardiography on a Mobile Platform.

The paper presents the design and high-fidelity prototype of the remote patient self-monitoring system using a combination of intelligent phonocardiography, mobile and web-based platforms. The advantage of self-monitoring is patient awareness about potential changes, the convenience of performing the measurement often, and the saving of the findings. A mobile platform enables a physician to see the data, get a summary of patient recordings, and as well as saving the data. We have designed two user profiles to enable such functionality and to enable consultations. During the three development iterations, two main prototypes were developed. In the patient prototype, the main functionality is measuring PCG signals, but with the possibility of reading more details about the results. In the physician's prototype, the main functionality is the patient overview, with the possibility of querying through old patient data to consult newer patients. For physicians to monitor patients monitoring themselves, the solution needs to be properly clinically validated and regulatory demands satisfy before it could be utilized in the Norwegian health domain.

Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification.

Convolutional Neural Network (CNN) has been widely proposed for different tasks of heart sound analysis. This paper presents the results of a novel study on the performance of a conventional CNN in comparison to the different architectures of recurrent neural networks combined with CNN for the classification task of abnormal-normal heart sounds. The study considers various combinations of parallel and cascaded integration of CNN with Gated Recurrent Network (GRN) as well as Long- Short Term Memory (LSTM) and explores the accuracy and sensitivity of each integration independently, using the Physionet dataset of heart sound recordings. The accuracy of the parallel architecture of LSTM-CNN reached 98.0% outperforming all the combined architectures, with a sensitivity of 87.2%. The conventional CNN offered sensitivity/accuracy of 95.9%/97.3% with far less complexity. Results show that a conventional CNN can appropriately perform and solely employed for the classification of heart sound signals.

User Needs of Young Czech Adults with Multiple Sclerosis in a Lifestyle App Design.

This paper presents a study that examined desired functionality, content, and design of a mobile application for young Czech adults living with Multiple Sclerosis (MS). The study was structured around a high-fidelity prototype developed for the corresponding user group in Norway. Both groups were active on social media and willing to contribute to designing an application promoting a healthy lifestyle and well-being. Adopting the content analysis, the study first compared the social content shared within the Facebook communities in the Norwegian and Czech user groups that were active. Regardless of the similarities, the Czech group expected that solutions regarding main functionalities and content should stand out from other competitive applications offered on the market. Most of all, they would like to see healthcare staff being engaged in content creation by providing credible information, especially regarding new treatments and clinical trials. Enhanced interaction between all the stakeholders (patients, and healthcare providers) would add value and relevance to the content already provided by social media.

Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification.

This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.

Information Technologies for Cognitive Decline.

Information technology (IT) is used to establish diagnosis and provide treatments for people with cognitive decline. The condition affects many before it becomes clear that more permanent changes, like dementia, could be noticed. Those who search for information are exposed to lots of information and different technologies which they need to make sense of and eventually use to help themselves. In this research, we have systematically analyzed the literature and information available on the Internet to systematically present methods used in diagnosing and treatment. We have also developed an artifact to help users obtain information with help of illustrations and text. The final user groups are all those for whom the cognitive decline is of concern. Medical professionals could be interested to direct their patients to use the artifact to gain information and keep learning at their own pace.

Designing a Dashboard for HIV-data Reporting Performance by Facilities: Case Study of Kenya.

Health management information systems implemented in low-and middle-income countries (LMICs) have provided availability of HIV-data. As such, dashboards have become increasingly popular as they provide a potentially powerful avenue for deriving insights at glance. This promotes use of data for decision-making by various stakeholders such as Ministries of Health as well as international donor organizations. Nonetheless, despite the use of dashboards in LMICs, their potential may go unrealized with underutilization of good design principles. In various LMICs, health facilities are required to submit HIV-indicator data on time for its use in decision-making. Hence, dashboards can be utilized in assessing facility reporting performance overtime in order to identify where interventions are needed. In this study, we applied good design principles in developing a dashboard, which presents the performance of facilities in reporting HIV-indicator data overtime (2011-2018). Timeliness and completeness in reporting were used as performance indicators and were extracted from the District Health Information Software Version 2 (DHIS2) in Kenya. Results for the system usability scale used in evaluating the dashboard was 87, which meant the dashboard usability was good.

Deep Time Growing Neural Network vs Convolutional Neural Network for Intelligent Phonocardiography.

This paper explores the capabilities of a sophisticated deep learning method, named Deep Time Growing Neural Network (DTGNN), and compares its possibilities against a generally well-known method, Convolutional Neural network (CNN). The comparison is performed by using time series of the heart sound signal, so-called Phonocardiography (PCG). The classification objective is to discriminate between healthy and patients with cardiac diseases by applying a deep machine learning method to PCGs. This approach which is called intelligent phonocardiography has received interest from the researchers toward the development of a smart stethoscope for decentralized diagnosis of heart disease. It is found that DTGNN associates further flexibility to the approach which enables the classifier to learn subtle contents of PCG, and meanwhile better copes with the complexities intrinsically that exist in the medical applications such as the imbalance training. The structural risk of the two methods is compared using the A-Test method.

Impact of Electronic Medical Records Systems in Reporting HIV Health Data Indicators in Kenya.

Electronic Medical Records Systems (EMRs) improve the quality of patient care and reduce medical errors. Nevertheless, their role in health data indicator reporting performance is unclear. We assessed reporting completeness and timeliness of HIV indicator data to the national aggregate reporting system, District Health Information Software 2 (DHIS2) in Kenya. We compared the reporting performance of facilities with and without EMRs implementation for the year 2013 as EMRs uptake was in progress. The comparative analysis involved 104 facilities implemented with and 152 without KenyaEMR system on three HIV programmatic areas. There were no statistically significant differences in performance regarding reporting completeness and timeliness by facilities with or without EMRs (p-values > 0.05 on all the three areas). The KenyaEMR system assessed in this study, therefore, cannot be associated with the transformed performance in reporting health indicators. This was probably due to the fact that the EMRs do not report electronically to DHIS2. Additional analysis can be conducted to compare reporting performance once data exchange functionality is fully established between KenyaEMR and DHIS2 systems.

Intellicor: Mobile Design for Monitoring Phonographic Signals.

A mobile and web-based prototype was developed to explore utility of heart sound data in the context of patient self-monitoring. There are not many applications available despite measurement equipment that can be purchased. This research aimed at developing an application that could help patients understand and use phonocardiography. The resulting prototype Intellicor enables easy-to-use web and mobile solutions such as registering heart sound, review of previous heart signal recordings, summaries of terms related to patient condition, and medication taken. These functions can be utilized by both patients and physicians to create understanding of heart signals and build communication as a part of treatment. Three development iterations included several expert evaluators who gave positive feedback on the concept of the application. It was appreciated that patients could monitor heart signals and better understand the results. The medical experts would welcome such a system into their work domain if developed correctly and in accordance with the formal expectations, both legal and clinical. The prototype has shown the advantage of gathering data otherwise impossible to obtain. The Intellicor prototype presents the foundation that ought to be further developed in close cooperation of clinical and biomedical experts. The self-monitoring of this kind could benefit patients and the healthcare sector as demonstrated by the Intellicor prototype.

A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods.

This paper presents an original method for studying the performance of the supervised Machine Learning (ML) methods, the A-Test method. The method offers the possibility of investigating the structural risk as well as the learning capacity of ML methods in a quantitating manner. A-Test provides a powerful validation method for the learning methods with small or medium size of the learning data, where overfitting is regarded as a common problem of learning. Such a condition can occur in many applications of bioinformatics and biomedical engineering in which access to a large dataset is a challengeable task. Performance of the A-Test method is explored by validation of two ML methods, using real datasets of heart sound signals. The datasets comprise of children cases with a normal heart condition as well as 4 pathological cases: aortic stenosis, ventricular septal defect, mitral regurgitation, and pulmonary stenosis. It is observed that the A-Test method provides further comprehensive and more realistic information about the performance of the classification methods as compared to the existing alternatives, the K-fold validation and repeated random sub-sampling.

Users' perception on factors contributing to electronic medical records systems use: a focus group discussion study in healthcare facilities setting in Kenya.

BACKGROUND: Electronic medical records systems (EMRs) adoption in healthcare to facilitate work processes have become common in many countries. Although EMRs are associated with quality patient care, patient safety, and cost reduction, their adoption rates are comparatively low. Understanding factors associated with the use of the implemented EMRs are critical for advancing successful implementations and scale-up sustainable initiatives. The aim of this study was to explore end users' perceptions and experiences on factors facilitating and hindering EMRs use in healthcare facilities in Kenya, a low- and middle-income country. METHODS: Two focus group discussions were conducted with EMRs users (n = 20) each representing a healthcare facility determined by the performance of the EMRs implementation. Content analysis was performed on the transcribed data and relevant themes derived. RESULTS: Six thematic categories for both facilitators and barriers emerged, and these related to (1) system functionalities; (2) training; (3) technical support; (4) human factors; (5) infrastructure, and (6) EMRs operation mode. The identified facilitators included: easiness of use and learning of the system complemented by EMRs upgrades, efficiency of EMRs in patient data management, responsive information technology (IT) and collegial support, and user training. The identified barriers included: frequent power blackouts, inadequate computers, retrospective data entry EMRs operation mode, lack of continuous training on system upgrades, and delayed IT support. CONCLUSIONS: Users generally believed that the EMRs improved the work process, with multiple factors identified as facilitators and barriers to their use. Most users perceived system functionalities and training as motivators to EMRs use, while infrastructural issues posed as the greatest barrier. No specific EMRs use facilitators and/or barriers could be attributed to facility performance levels. Continuous evaluations are necessary to assess improvements of the identified factors as well as determine emerging issues.

A multivariate statistical evaluation of actual use of electronic health record systems implementations in Kenya.

BACKGROUND: Health facilities in developing countries are increasingly adopting Electronic Health Records systems (EHRs) to support healthcare processes. However, only limited studies are available that assess the actual use of the EHRs once adopted in these settings. We assessed the state of the 376 KenyaEMR system (national EHRs) implementations in healthcare facilities offering HIV services in Kenya. METHODS: The study focused on seven EHRs use indicators. Six of the seven indicators were programmed and packaged into a query script for execution within each KenyaEMR system (KeEMRs) implementation to collect monthly server-log data for each indicator for the period 2012-2019. The indicators included: Staff system use, observations (clinical data volume), data exchange, standardized terminologies, patient identification, and automatic reports. The seventh indicator (EHR variable Completeness) was derived from routine data quality report within the EHRs. Data were analysed using descriptive statistics, and multiple linear regression analysis was used to examine how individual facility characteristics affected the use of the system. RESULTS: 213 facilities spanning 19 counties participated in the study. The mean number of authorized users who actively used the KeEMRs was 18.1% (SD = 13.1%, p<0.001) across the facilities. On average, the volume of clinical data (observations) captured in the EHRs was 3363 (SD = 4259). Only a few facilities(14.1%) had health data exchange capability. 97.6% of EHRs concept dictionary terms mapped to standardized terminologies such as CIEL. Within the facility EHRs, only 50.5% (SD = 35.4%, p< 0.001) of patients had the nationally-endorsed patient identifier number recorded. Multiple regression analysis indicated the need for improvement on the mode of EHRs use of implementation. CONCLUSION: The standard EHRs use indicators can effectively measure EHRs use and consequently determine success of the EHRs implementations. The results suggest that most of the EHRs use areas assessed need improvement, especially in relation to active usage of the system and data exchange readiness.

Identifying barriers and facilitators in HIV-indicator reporting for different health facility performances: A qualitative case study.

Identifying barriers and facilitators in HIV-indicator reporting contributes to strengthening HIV monitoring and evaluation efforts by acknowledging contributors to success, as well as identifying weaknesses within the system that require improvement. Nonetheless, there is paucity in identifying and comparing barriers and facilitators in HIV-indicator data reporting among facilities that perform well and those that perform poorly at meeting reporting completeness and timeliness requirements. Therefore, this study aims to use a qualitative approach in identifying and comparing the current state of barriers and facilitators in routine reporting of HIV-indicators by facilities performing well, and those performing poorly in meeting facility reporting completeness and timeliness requirements to District Health Information Software2 (DHIS2). A multiple qualitative case study design was employed. The criteria for case selection was based on performance in HIV-indicator facility reporting completeness and timeliness. Areas of interest revolved around reporting procedures, organizational, behavioral, and technical factors. Purposive sampling was used to identify key informants in the study. Data was collected using semi-structured in-depth interviews with 13 participants, and included archival records on facility reporting performance, looking into documentation, and informal direct observation at 13 facilities in Kenya. Findings revealed that facilitators and barriers in reporting emerged from the following factors: interrelationship between workload, teamwork and skilled personnel, role of an EMRs system in reporting, time constraints, availability and access-rights to DHIS2, complexity of reports, staff rotation, availability of trainings and mentorship, motivation, availability of standard operating procedures and resources. There was less variation in barriers and facilitators faced by facilities performing well and those performing poorly. Continuous evaluations have been advocated within health information systems literature. Therefore, continuous qualitative assessments are also necessary in order to determine improvements and recurring of similar issues. These assessments have also complemented other quantitative analyses related to this study.

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm.

BACKGROUND: The ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV-indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time. The aim of this study is to conduct a comprehensive assessment of the reporting status for HIV-indicators, from the time of DHIS2 implementation, using Kenya as a case study. METHODS: A retrospective observational study was conducted to assess reporting performance of health facilities providing any of the HIV services in all 47 counties in Kenya between 2011 and 2018. Using data extracted from DHIS2, K-means clustering algorithm was used to identify homogeneous groups of health facilities based on their performance in meeting timeliness and completeness facility reporting requirements for each of the six programmatic areas. Average silhouette coefficient was used in measuring the quality of the selected clusters. RESULTS: Based on percentage average facility reporting completeness and timeliness, four homogeneous groups of facilities were identified namely: best performers, average performers, poor performers and outlier performers. Apart from blood safety reports, a distinct pattern was observed in five of the remaining reports, with the proportion of best performing facilities increasing and the proportion of poor performing facilities decreasing over time. However, between 2016 and 2018, the proportion of best performers declined in some of the programmatic areas. Over the study period, no distinct pattern or trend in proportion changes was observed among facilities in the average and outlier groups. CONCLUSIONS: The identified clusters revealed general improvements in reporting performance in the various reporting areas over time, but with noticeable decrease in some areas between 2016 and 2018. This signifies the need for continuous performance monitoring with possible integration of machine learning and visualization approaches into national HIV reporting systems.

Development of standard indicators to assess use of electronic health record systems implemented in low-and medium-income countries.

BACKGROUND: Electronic Health Record Systems (EHRs) are being rolled out nationally in many low- and middle-income countries (LMICs) yet assessing actual system usage remains a challenge. We employed a nominal group technique (NGT) process to systematically develop high-quality indicators for evaluating actual usage of EHRs in LMICs. METHODS: An initial set of 14 candidate indicators were developed by the study team adapting the Human Immunodeficiency Virus (HIV) Monitoring, Evaluation, and Reporting indicators format. A multidisciplinary team of 10 experts was convened in a two-day NGT workshop in Kenya to systematically evaluate, rate (using Specific, Measurable, Achievable, Relevant, and Time-Bound (SMART) criteria), prioritize, refine, and identify new indicators. NGT steps included introduction to candidate indicators, silent indicator ranking, round-robin indicator rating, and silent generation of new indicators. 5-point Likert scale was used in rating the candidate indicators against the SMART components. RESULTS: Candidate indicators were rated highly on SMART criteria (4.05/5). NGT participants settled on 15 final indicators, categorized as system use (4); data quality (3), system interoperability (3), and reporting (5). Data entry statistics, systems uptime, and EHRs variable concordance indicators were rated highest. CONCLUSION: This study describes a systematic approach to develop and validate quality indicators for determining EHRs use and provides LMICs with a multidimensional tool for assessing success of EHRs implementations.

Data cleaning process for HIV-indicator data extracted from DHIS2 national reporting system: a case study of Kenya.

BACKGROUND: The District Health Information Software-2 (DHIS2) is widely used by countries for national-level aggregate reporting of health-data. To best leverage DHIS2 data for decision-making, countries need to ensure that data within their systems are of the highest quality. Comprehensive, systematic, and transparent data cleaning approaches form a core component of preparing DHIS2 data for analyses. Unfortunately, there is paucity of exhaustive and systematic descriptions of data cleaning processes employed on DHIS2-based data. The aim of this study was to report on methods and results of a systematic and replicable data cleaning approach applied on HIV-data gathered within DHIS2 from 2011 to 2018 in Kenya, for secondary analyses. METHODS: Six programmatic area reports containing HIV-indicators were extracted from DHIS2 for all care facilities in all counties in Kenya from 2011 to 2018. Data variables extracted included reporting rate, reporting timeliness, and HIV-indicator data elements per facility per year. 93,179 facility-records from 11,446 health facilities were extracted from year 2011 to 2018. Van den Broeck et al.'s framework, involving repeated cycles of a three-phase process (data screening, data diagnosis and data treatment), was employed semi-automatically within a generic five-step data-cleaning sequence, which was developed and applied in cleaning the extracted data. Various quality issues were identified, and Friedman analysis of variance conducted to examine differences in distribution of records with selected issues across eight years. RESULTS: Facility-records with no data accounted for 50.23% and were removed. Of the remaining, 0.03% had over 100% in reporting rates. Of facility-records with reporting data, 0.66% and 0.46% were retained for voluntary medical male circumcision and blood safety programmatic area reports respectively, given that few facilities submitted data or offered these services. Distribution of facility-records with selected quality issues varied significantly by programmatic area (p < 0.001). The final clean dataset obtained was suitable to be used for subsequent secondary analyses. CONCLUSIONS: Comprehensive, systematic, and transparent reporting of cleaning-process is important for validity of the research studies as well as data utilization. The semi-automatic procedures used resulted in improved data quality for use in secondary analyses, which could not be secured by automated procedures solemnly.

K-Means Clustering in Monitoring Facility Reporting of HIV Indicator Data: Case of Kenya.

Health management information systems (HMISs) in low- and middle-income countries have been used to collect large amounts of data after years of implementation, especially in support of HIV care services. National-level aggregate reporting data derived from HMISs are essential for informed decision-making. However, the optimal statistical approaches and algorithms for deriving key insights from these data are yet to be fully and adequately utilized. This paper demonstrates use of the k-means clustering algorithm as an approach in supporting monitoring of facility reporting and data-informed decision-making, using the case example of Kenya HIV national reporting data. Results reveal four homogeneous cluster categories that can be used in assessing overall facility performance and rating of that performance.

Assessment of HIV Data Reporting Performance by Facilities During EMR Systems Implementations in Kenya.

There is little evidence that implementations of Electronic Medical Record Systems (EMRs) are associated with better reporting completeness and timeliness of HIV routine data to the national aggregate system. We analyzed the reporting completeness and timeliness of HIV reports to Kenya's national aggregate reporting system from District Health Information Software 2 (DHIS2) for the period 2011 to 2018. On average, reporting completeness improved to 97% whilst timeliness increased to 83% in 2017 with similar performance for the facilities under study that implemented either KenyaEMR or IQCare. However, in 2018, the reporting rates dropped by 13% for completeness and 11% for timeliness most likely due to changed reporting procedures. This suggests that besides EMRs, there are other factors influencing reporting such as reporting routines, which need to be assessed separately. Nonetheless, the EMRs have facilitated the collection of HIV data for submission to the DHIS2, which in turn facilitates the reporting process for the data officers.

Integrating Socially Assistive Robots into Japanese Nursing Care.

This paper presents experiences of integrating assistive robots in Japanese nursing care through semi-structured interviews and site observations at three nursing homes in Japan during the year 2019. The study looked at experiences with the robots Paro, Pepper, and Qoobo. The goal was to investigate and evaluate the current state of using robots within the nursing care context, which involved: firsthand experiences with intended and real users; and response from the elderly, and nursing staff. The qualitative analysis results pointed out user satisfaction, adjusted purpose, therapeutic and entertaining effects. The potentials of using robots to assist in elderly care has been evident. Limitations currently relate to the lack of ways to fully utilize and integrate robots.

Distinguishing Septal Heart Defects from the Valvular Regurgitation Using Intelligent Phonocardiography.

This paper presents an original machine learning method for extracting diagnostic medical information from heart sound recordings. The method is proposed to be integrated with an intelligent phonocardiography in order to enhance diagnostic value of this technology. The method is tailored to diagnose children with heart septal defects, the pathological condition which can bring irreversible and sometimes fatal consequences to the children. The study includes 115 children referrals to an university hospital, consisting of 6 groups of the individuals: atrial septal defects (10), healthy children with innocent murmur (25), healthy children without any murmur (25), mitral regurgitation (15), tricuspid regurgitation (15), and ventricular septal defect (25). The method is trained to detect the atrial or ventricular septal defects versus the rest of the groups. Accuracy/sensitivity and the structural risk of the method is estimated to be 91.6%/88.4% and 9.89%, using the repeated random sub sampling and the A-Test method, respectively.