An Application of eHealth Technology Toward the Digitization of the Health Records of Older Patients With Cochlear Implants

2019 ◽  
Vol 28 (3S) ◽  
pp. 796-801 ◽  
Author(s):  
Gabriella Tognola ◽  
Alessandra Murri ◽  
Domenico Cuda
Keyword(s):  
Care Service ◽  
Clinical Information ◽  
Health Data ◽  
Automated System ◽  
Cognitive Computing ◽  
Health Records ◽  
Good Recall ◽  
Digital Format ◽  
Before And After ◽  
Medical Reports

Purpose Despite the current legislative indications toward the digitization of patient health records, 80% of health data are unstructured and in a format that cannot be used in electronic archives or in registries of diseases. An innovative automated system is here proposed to efficiently retrieve and digitize clinical information from original unstructured ear, nose, and throat (ENT) medical records, in order to reduce the manual workload in the retrieval and digitization process. Method The system, based on an eHealth technology named cognitive computing , interprets medical reports to transform unstructured clinical data (e.g., narrative text) into a structured digital format. The system has been tailored to handle the reports of aged cochlear implant (CI) patients by digitizing the information typically requested in electronic CI registries and by the current ENT/audiology guidelines. Results were obtained from the reports generated by an outpatient ENT care service from 52 older adult CI patients. Results The system allowed a quick and automated interpretation and retrieval of all the information required, such as the patient's medical history, risk factors, examination outcomes, communicative performances before and after CI implantation, and CI settings. The accuracy of the system in correctly interpreting and retrieving the above information from the original unstructured medical reports was very good (recall = 0.78; precision = 0.95). The system allowed to reduce the time needed to manually digitize the information from 20–30 min/report to only 20 s/report. Conclusion The proposed system is a viable solution for the automated digitization of unstructured health data as recommended by the ENT/audiology clinical best practices.

scholarly journals National data opt out programme: consequences for maternity services in England

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Kate Marie Lewis ◽  
Pia Hardelid
Keyword(s):  
Care Service ◽  
Population Level ◽  
Health Data ◽  
Service Improvement ◽  
National Data ◽  
Health Records ◽  
Opting Out ◽  
Individual Care ◽  
Opt Out ◽  
English Population

Electronic health records offer great potential for individual care, service improvement and, when collated, the health of the wider population. Datasets composed of these types of records have been invaluable to our understanding of risk factors for maternal and infant ill-health. However, a potential barrier to data quality in England is emerging where patients choose to opt out of sharing their information beyond the NHS. Focussing on maternity statistics, we will present the importance of population level health data for monitoring NHS services, and the potential consequences for patients of opting out. Evidencing the success of similar systems in Nordic countries, we argue that the English population must be better informed of the implications of opting out of sharing NHS data for research and the safeguards in place to protect patient information.

scholarly journals Evaluation of an initiative to improve advance care planning for a home-based primary care service

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michelle B. Cox ◽  
Margaret J. McGregor ◽  
Madison Huggins ◽  
Paige Moorhouse ◽  
Laurie Mallery ◽  
...  
Keyword(s):  
Older Adults ◽  
Primary Care ◽  
Decision Maker ◽  
Care Service ◽  
Do Not Resuscitate ◽  
Substitute Decision Maker ◽  
Home Based Primary Care ◽  
Home Based ◽  
Before And After ◽  
Advance Care

Abstract Background Advance care planning (ACP) is a process that enables individuals to describe, in advance, the kind of health care they would want in the future. There is evidence that ACP reduces hospital-based interventions, especially at the end of life. ACP for frail older adults is especially important as this population is more likely to use hospital services but less likely to benefit from resource intensive care. Our study goal was to evaluate whether an approach to ACP developed for frail older adults, known as the Palliative and Therapeutic Harmonization or PATH, demonstrated an improvement in ACP. Methods The PATH approach was adapted to a primary care service for homebound older adults in Vancouver, Canada. This retrospective chart review collected surrogate measures related to ACP from 200 randomly selected patients enrolled in the service at baseline (prior to June 22, 2017), and 114 consecutive patients admitted to the program after implementation of the PATH ACP initiative (October 1, 2017 to May 1, 2018). We compared the following surrogate markers of ACP before and after implementation of the PATH model, chart documentation of: frailty stage, substitute decision-maker, resuscitation decision, and hospitalization decision. A composite ACP documentation score that ascribed one point for each of the above four measures (range 0 to 4) was also compared. For those with documented resuscitation and hospitalization decisions, the study examined patient/ substitute decision-maker expressed preferences for do-not-resuscitate and do-not-hospitalize, before and after implementation. Results We found the following changes in ACP-related documentation before and after implementation: frailty stage (27.0% versus 74.6%, p < .0001); substitute decision-maker (63.5% versus 71.9%, p = 0.128); resuscitation decision documented (79.5% versus 67.5%, p = 0.018); and hospitalization decision documented (61.5% versus 100.0%, p < .0001); mean (standard deviation) composite ACP documentation score (2.32 (1.16) versus 3.14 (1.11), p < .0001). The adjusted odds ratios (95% confidence intervals) for an expressed preference of do-not-resuscitate and do-not-hospitalize after implementation were 0.87 (0.35, 2.15) and 3.14 (1.78, 5.55), respectively. Conclusions Results suggest partial success in implementing the PATH approach to ACP in home-based primary care. Key contextual enablers and barriers are important considerations for successful implementation.

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

scholarly journals Interrelationship between radiologic findings and prognosis of epilepsy in children with neurocysticercosis

2002 ◽  
Vol 60 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Lisiane Seguti Ferreira ◽  
Verônica A. Zanardi ◽  
Min Li Li ◽  
Marilisa M. Guerreiro
Keyword(s):  
Disease Activity ◽  
Seizure Frequency ◽  
Statistical Difference ◽  
Seizure Control ◽  
Clinical Information ◽  
Radiologic Findings ◽  
Comparison Results ◽  
Before And After ◽  
Evolutive Stage ◽  
The Relationship

INTRODUCTION: Epileptic manifestations of Neurocysticercosis (NC) appear to depend on number and localization of the cysts. The objective of this study was to investigate the relationship between CT findings, number of parasites and the evolutive stage of the cysts, and the prognosis of epilepsy in children with NC. METHOD: We studied 28 patients with the parenchymal form of NC, considering: epilepsy duration; seizure frequency before and after AED treatment; seizure control; number of AED and recurrence after AED withdrawal. Clinical information was crossed with the number of lesions and disease activity in univariate comparison. RESULTS: The analysis of the clinical data in relation to the number of lesions and disease activity showed no statistical difference among the variables (p>0.05). CONCLUSION: We conclude that the course of epilepsy due to NC in childhood cannot be based exclusively on the number or stage of the parasites.

scholarly journals MO529PREVALENCE AND DETERMINANT FACTORS OF DEPRESSION AND ANXIETY IN PEOPLE WITH CHRONIC KIDNEY DISEASE : A MOROCCAN CROSS-SECTIONAL  STUDY

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Amina Chrifi Alaoui ◽  
Mohammed Omari ◽  
Noura Qarmiche ◽  
Omar Kouiri ◽  
Basmat Amal Chouhani ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Depression Scale ◽  
Clinical Information ◽  
Cross Sectional Study ◽  
University Hospital ◽  
Depression And Anxiety ◽  
Cross Sectional ◽  
Determinant Factors ◽  
Before And After

Abstract Background and Aims The Chronic kidney disease (CKD), like many chronic illnesses, is invariably associated with various psychiatric conditions and poorer quality of life. This study aims to assess the prevalence of depression and anxiety among CKD patient and their determinant factors. Method this is a cross sectional single center study in a Moroccan university hospital. Patients aged ≥ 18 years old and followed for more than one year were included. The data was collected using a questionnaire for sociodemographic and clinical information and the Hospital anxiety and depression scale (HADS) to assess depression and anxiety prevalence. After the description of the population’s characteristics, the statistical analysis aimed to assess the association between depression and anxiety disorders and the estimated glomerular filtration rate before and after adjustment on several confounding factors. Results 88 patients were included (63.6% of them were women, the mean age was 61.8±14.0 years), 21 were on stage 3, 46 were on stage 4, and 21 were on stage 5 of the CKD. The median of depression sub-score was 5.00[2.00; 10.0], the median of anxiety sub-score was 6.00[4.00; 9.00], and the median of the global score was 11.0[7.00; 20.0], 22.0% of included patients had depression and 22.0% had anxiety. Both depression and anxiety scores were associated to eGFR before and after adjustment (p= 0.001, p&lt;0.001and p=0.04, p=0.03 respectively). Conclusion This study showed that depression and anxiety are strongly related to the CKD progression, which should motivate both doctors and nurses to improve their psychological care toward CKD patients.

scholarly journals Big health data: the new frontier for cardiovascular disease

2020 ◽  
pp. 4-7
Keyword(s):  
Cardiovascular Disease ◽  
Population Studies ◽  
Large Population ◽  
Cost Effective ◽  
Health Data ◽  
Health Care Administrators ◽  
Health Records ◽  
Traditional Research ◽  
New Frontier ◽  
The World

Despite improvement in diagnosis and management, cardiovascular disease (CVD) is the leading cause of death and hospitalization throughout the world. The expansion of digital cardiology presents outstanding opportunities for clinicians, researchers, and health care administrators to improve outcomes and sustainability of health systems. Electronic big health data combining electronic health records (EHRs) from diverse individuals across a wide variety of platforms may provide a real-time solution to questions and problems relating to health. Very large population studies based on EHR are efficient and cost-effective, and offer an alternative to traditional research approaches. Indeed, digital cardiology can help researchers to enhance, diagnose, and manage CVD using dedicated algorithms that allow targeted and personalized CVD treatments

Big Data Analytics in Healthcare

2021 ◽  
pp. 85-98
Author(s):  
Sam Goundar ◽  
Karpagam Masilamani ◽  
Akashdeep Bhardwaj ◽  
Chandramohan Dhasarathan
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Health Data ◽  
Massive Data ◽  
Healthcare Industry ◽  
Health Records ◽  
Healthcare Data ◽  
Data Formats ◽  
Analytical Tools ◽  
Different Sources

This chapter provides better understanding and use-cases of big data in healthcare. The healthcare industry generates lot of data every day, and without proper analytical tools, it is quite difficult to extract meaningful data. It is essential to understand big data tools since the traditional devices don't maintain this vast data, and big data solves the major issue in handling massive healthcare data. Health data from numerous health records are collected from various sources, and this massive data is put together to form the big data. Conventional database cannot be used in this purpose due to the diversity in data formats, so it is difficult to merge, and so it is quite impossible to process. With the use of big data this problem is solved, and it can process highly variable data from different sources.

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