A Multi-model Approach in Developing an Intelligent Assistant for Diagnosis Recommendation in Clinical Health Systems

Clinical health information systems capture massive amounts of unstructured data from various health and medical facilities. This study utilizes unstructured patient clinical text data to develop an intelligent assistant that can identify possible related diagnoses based on a given text input. The approach applies a one-vs-rest binary classification technique wherein given an input text data, it is identified whether it can be positively or negatively classified for a given diagnosis. Multi-layer Feed-Forward Neural Network models were developed for each individual diagnosis case. The task of the intelligent assistant is to iterate over all the different models and return those that output a positive diagnosis. To validate the performance of the models, the performance metrics were compared against Naive Bayes, Decision Trees, and K-Nearest Neighbor. The results show that the neural network learner provided better performance scores in both accuracy and area under the curve metric scores. Further, testing on multiple diagnoses also shows that the methodology for developing the diagnosis models can be replicated for development of models for other diseases as well

Towards developing an intelligent agent to assist in patient diagnosis using neural networks on unstructured patient clinical notes: Initial analysis and models

Technological advances in information-communication technologies in the health ecosystem have allowed for the recording and consumption of massive amounts of structured and unstructured health data. In developing countries, the use of Electronic Medical Records (EMR) is necessary to address the need for efficient delivery of services and informed decision-making, especially at the local level where health facilities and practitioners may be lacking. Text mining is a variation of data mining that tries to extract non-trivial information and knowledge from unstructured text. This study aims to determine the feasibility of integrating an intelligent agent within EMRs for automatic diagnosis prediction based on the unstructured clinical notes. A Multilayer Feed- Forward Neural Network with Back Propagation training was implemented for classification. The two neural network models predicted hypertension against similar diagnoses with 11.52% and 10.53% percent errors but predicted with 54.01% and 64.82% percent errors when used on a group of similar diagnoses. Further development is needed for prediction of diagnoses with common symptoms and related diagnoses. The results still prove, however, that unstructured data possesses value beneficial for clinical decision support. If further analyzed with structured data, a more accurate intelligent agent may be explored

A Micro-analysis Approach in Understanding Electronic Medical Record Usage in Rural Communities: Comparison of Frequency of Use on Performance Before and During the COVID-19 Pandemic

In strengthening eHealth in the Philippines to support the universal health care (UHC) law, the scaling up and full adoption of electronic medical record (EMR) systems was strategically scheduled and supposedly completed in 2020. The Covid-19 pandemic, however, delayed these strengthening efforts. We wanted to assess the status of EMR adoption in primary clinics of rural health units (RHUs) and understand the frequency of use, particularly during the pandemic. Through analyses of EMR usage logs from selected RHUs in 2020, we estimated frequency of EMR usage based on duration of use and tested if this was influenced by the performing RHU and pandemic event. We also determined the most frequent EMR activities through process maps and tested if there were differences in the conduct of these activities before and during the pandemic. Results showed that EMR use during work hours was significantly dependent on the performing RHU (

The 2019 Philippine UHC Act, Pandemic Management and Implementation Implications in a Post-COVID-19 World: A Content Analysis

The 2019 Philippine Universal Health Care Act (Republic Act 11223) was set for implementation in January 2020 when disruptions brought on by the pandemic occurred. Will the provisions of the new UHC Act for an improved health system enable agile responses to forthcoming shocks, such as this COVID-19 pandemic? A content analysis of the 2019 Philippine UHC Act can identify neglected and leverage areas for systems’ improvement in a post-pandemic world. While content or document analysis is commonly undertaken as part of scoping or systematic reviews of a qualitative nature, quantitative analyses using a two-way mixed effects, consistency, multiple raters type of intraclass correlation coefficient (ICC) were applied to check for reliability and consistency of agreement among the study participants in the manual tagging of UHC components in the legislation. The intraclass correlation reflected the individuals’ consistency of agreement with significant reliability (0.939, p \u3c 0.001). The assessment highlighted a centralized approach to implementation, which can set aside the crucial collaborations and partnerships demonstrated and developed during the pandemic. The financing for local governments was strengthened with a new ruling that could alter UHC integration tendencies. A smarter allocation of tax-based financing sources, along with strengthened information and communications systems, can confront issues of trust and accountability, amidst the varying capacities of agents and systems

A Micro-analysis Approach in Understanding Electronic Medical Record Usage in Rural Communities : Comparison of Frequency of Use on Performance Before and During the COVID-19 Pandemic

In strengthening eHealth in the Philippines to support the universal health care (UHC) law, the scaling up and full adoption of electronic medical record (EMR) systems was strategically scheduled and supposedly completed in 2020. The Covid-19 pandemic, however, delayed these strengthening efforts. We wanted to assess the status of EMR adoption in primary clinics of rural health units (RHUs) and understand the frequency of use, particularly during the pandemic. Through analyses of EMR usage logs from selected RHUs in 2020, we estimated frequency of EMR usage based on duration of use and tested if this was influenced by the performing RHU and pandemic event. We also determined the most frequent EMR activities through process maps and tested if there were differences in the conduct of these activities before and during the pandemic. Results showed that EMR use during work hours was significantly dependent on the performing RHU (p<0.001). High-performing RHUs used EMRs more than 3 hours/day while low-performing RHUs used the systems for less. The pandemic either significantly decreased or increased EMR use during work hours by around 5 hours/day in some RHUs (p<0.01). Process maps revealed that there were additional activities performed by RHUs during the pandemic. Except for Update Patient Profile and Add Patient EMR features, significant differences (p<0.01) were observed in accessing frequently used features before and during the pandemic. The results suggest some uneven level of utilization of EMRs at the primary care level which can impact readiness to support full implementation of the UHC law. The study shows the potential of using a more granular approach in studying adoption to help improve the quality of EMR use and contribute to improving health service delivery and financing.publishedVersionPeer reviewe

Clinical Interactions in Electronic Medical Records Towards the Development of a Token-Economy Model

The use of electronic medical records (EMRs) plays a crucial role in the successful implementation of the Universal Healthcare Law which promises quality and affordable healthcare to all Filipinos. Consequently, the current adoption of EMRs should be studied from the perspective of the healthcare provider. As most studies look into use of EMRs by doctors or patients, there are very few that extend studies to look at possible interaction of doctor and patient in the same EMR environment. Understanding this interaction paves the way for possible incentives that will increase the use and adoption of the EMR. This study uses process mining to understand simulated doctor-patient interaction, with the goal of developing interaction features and a token economy framework to increase EMR adoption. Results from the process mining showed that current EMR interaction remains low, and highlighted the need for interaction features to promote preventive healthcare. Moreover, process mining from the simulated logs showed that consistency and time are important factors in encouraging usage. Activity category, relative frequency of activity, relative case frequency of activity and average time spent on activity are features that may serve as the foundation for a token economy framework for EMRs

Development of SIANN : Shine Intelligent Assistant using Neural Networks for electronic medical records

Technological advances in information-communication technologies in the health ecosystem have allowed for the processing of massive amounts of structured and unstructured health data. In developing countries, the use of Electronic Medical Records (EMR) i

User-centered Approach to Developing Solutions for Electronic Medical Records: Extending EMR Data Entry

The rapid advancement of technology presents the opportunity to digitize practice management. With a doctor to patient ratio of 1:33,000, digitizing health records in the Philippines is seen as one solution in providing more efficient health care services. With the deployment of EMRs in the Philippines at its infancy, there is a need to initiate studies on feasibility, usability and user perception. This paper reports findings on usability of EMRs in a developing economy. Specifically, a system usability scale (SUS) was used to assess the usability of an EMR and interviews were conducted to acquire user feedback. Results of the survey indicated an overall mean SUS score of 70.76 with age and confidence in technology being key deciding factors. Further observations and future research to streamline the heavy task of encoding on an EMR during patient-physician consultation are explained

Towards an On-line Handwriting Recognition Interface for Health Service Providers using Electronic Medical Records

The 2019 Universal Health Care Act in the Philippines has allowed healthcare service providers to have a second look at using electronic medical records (EMRs) in their practice with tools that enable servicing the poorest of the poor and coursing payments via EMR. A review of first world country narratives, however, show evidence of the substandard usability of EMRs. Physician work is impeded as almost two-thirds of consultation time is spent documenting on an EMR instead conversing with patients face-to-face. This paper describes a handwriting recognition interface for EMR data entry that is user-friendly and is unobstructive to the patient-physician relationship. An initial prototype tested by medical students showed a handwriting recognition accuracy of 34% while a second testing by health service providers showed a handwriting recognition accuracy of 42%. Findings show that recognition is challenged by specialized words and accidental markings which cause extra spaces and extra symbols. Additional features to the system as well as possible augmentations to improve accuracy and efficiency through ontology, machine learning, and AI are also roadmapped

Management of Health- and Disaster-Related Data

Prolonged health emergencies and disasters greatly affect health and well-being of individuals and communities. Past experiences on extreme emergencies and disasters have taught communities the value of preparedness. Information is key in responding to health crises especially in areas where health capacity is challenged. This chapter explains the necessity of identifying appropriate health and disaster data and proposes its transformation to information needed for decision-making. It presents different examples of systems and datasets that were used for the management of response during disasters and extreme emergencies. By introducing examples from Japan and Philippines; this chapter also points out that aside from medical data; nonmedical data; such as lifestyle and hygiene information; are necessary to protect the health of disaster victims.The objective of disaster response is to ensure that no one is left behind. It is imperative therefore that disaster response is complemented with targeted information. We recognized difficulties in community monitoring such as lack of geographic-specific information; no standard for minimum health security indicator; limited availability to submit data; and variances in need for meaningful information. There are also challenges in visualizing uncountable data; real-time updating of disaster situations; and accurate statistics disaggregated by characteristics. At the core of decision-making is the appropriate transformation of data to meaningful information. Utilization of data now becomes one of the essential adaptive technologies that needs to be provided at the local level. The challenge lies in preferential options in collecting and storing disaster- and health- and non-health-related data. Although the international initiatives expend significant effort to produce data and maps for the Health EDRM; this review considers the producers and end-users of the data products or how the data was used with the objective of studying mechanisms on how to improve on the product