scholarly journals Mining Electronic Health Records to Promote the Reach of Digital Interventions for Cancer Prevention Through Proactive Electronic Outreach: Protocol for the Mixed Methods OptiMine Study

10.2196/23669 ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. e23669
Author(s):  
Michael S Amato ◽  
Sherine El-Toukhy ◽  
Lorien C Abroms ◽  
Henry Goodfellow ◽  
Alex T Ramsey ◽  
...  
Keyword(s):  
At Risk ◽  
Smoking Cessation ◽  
Mixed Methods ◽  
Electronic Health Records ◽  
Behavior Change ◽  
Health Records ◽  
Digital Interventions ◽  
Behavior Change Interventions ◽  
Electronic Health ◽  
Message System

Background Digital behavior change interventions have demonstrated effectiveness for smoking cessation and reducing alcohol intake, which ultimately reduce cancer risk. Leveraging electronic health records (EHR) to identify at-risk patients and increasing the reach of digital interventions through proactive electronic outreach provide a novel approach that may increase the number of individuals who engage with evidence-based treatment. Objective This study aims to increase the reach of digital behavior change interventions by implementing a proactive electronic message system for smoking cessation and alcohol reduction among a large, at-risk population identified through an acute hospital EHR. Methods This protocol describes a 3-phase, mixed-methods implementation study to assess the acceptability, feasibility, and reach of a proactive electronic message system to digital interventions using a hospital’s EHR system to identify eligible patients. In Phase 1, we will conduct focus group discussions with patients and hospital staff to assess the overall acceptability of the electronic message system. In Phase 2, we will conduct a descriptive analysis of the patient population in the hospital EHR regarding target risk behaviors and other person-level characteristics to determine the project’s feasibility and potential reach. In Phase 3, we will send proactive messages to patients identified as smokers or risky drinkers. Messages will encourage and provide access to behavior change mobile apps via an embedded link; the primary outcome will be the proportion of participants who click on the link to access information about the apps. Results At the time of initial protocol submission, data collection was complete, but analysis had not begun. This study was funded by Cancer Research UK from April 2019 to March 2020. Health Research Authority approval was granted in June 2019. Conclusions Increasing the reach of digital behavior change interventions can improve population health by reducing the burden of preventable death and disease. International Registered Report Identifier (IRRID) DERR1-10.2196/23669

scholarly journals Mining Electronic Health Records to Promote the Reach of Digital Interventions for Cancer Prevention Through Proactive Electronic Outreach: Protocol for the Mixed Methods OptiMine Study (Preprint)

2020 ◽  
Author(s):  
Michael S Amato ◽  
Sherine El-Toukhy ◽  
Lorien C Abroms ◽  
Henry Goodfellow ◽  
Alex T Ramsey ◽  
...  
Keyword(s):  
At Risk ◽  
Smoking Cessation ◽  
Mixed Methods ◽  
Electronic Health Records ◽  
Behavior Change ◽  
Health Records ◽  
Digital Interventions ◽  
Behavior Change Interventions ◽  
Electronic Health ◽  
Message System

BACKGROUND Digital behavior change interventions have demonstrated effectiveness for smoking cessation and reducing alcohol intake, which ultimately reduce cancer risk. Leveraging electronic health records (EHR) to identify at-risk patients and increasing the reach of digital interventions through proactive electronic outreach provide a novel approach that may increase the number of individuals who engage with evidence-based treatment. OBJECTIVE This study aims to increase the reach of digital behavior change interventions by implementing a proactive electronic message system for smoking cessation and alcohol reduction among a large, at-risk population identified through an acute hospital EHR. METHODS This protocol describes a 3-phase, mixed-methods implementation study to assess the acceptability, feasibility, and reach of a proactive electronic message system to digital interventions using a hospital’s EHR system to identify eligible patients. In Phase 1, we will conduct focus group discussions with patients and hospital staff to assess the overall acceptability of the electronic message system. In Phase 2, we will conduct a descriptive analysis of the patient population in the hospital EHR regarding target risk behaviors and other person-level characteristics to determine the project’s feasibility and potential reach. In Phase 3, we will send proactive messages to patients identified as smokers or risky drinkers. Messages will encourage and provide access to behavior change mobile apps via an embedded link; the primary outcome will be the proportion of participants who click on the link to access information about the apps. RESULTS At the time of initial protocol submission, data collection was complete, but analysis had not begun. This study was funded by Cancer Research UK from April 2019 to March 2020. Health Research Authority approval was granted in June 2019. CONCLUSIONS Increasing the reach of digital behavior change interventions can improve population health by reducing the burden of preventable death and disease. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/23669

scholarly journals UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records

2020 ◽  
Author(s):  
Jemma L Walker ◽  
Daniel J Grint ◽  
Helen Strongman ◽  
Rosalind M Eggo ◽  
Maria Peppa ◽  
...  
Keyword(s):  
At Risk ◽  
Electronic Health Records ◽  
Health Condition ◽  
Point Prevalence ◽  
Health Conditions ◽  
Risk Population ◽  
Health Records ◽  
Population At Risk ◽  
Electronic Health ◽  
The Uk

Background This study aimed to describe the population at risk of severe COVID-19 due to underlying health conditions across the United Kingdom in 2019. Methods We used anonymised electronic health records from the Clinical Practice Research Datalink GOLD to describe the point prevalence on 5 March 2019 of the at-risk population following national guidance. Prevalence for any risk condition and for each individual condition is given overall and stratified by age and region. We repeated the analysis on 5 March 2014 for full regional representation and to describe prevalence of underlying health conditions in pregnancy. We additionally described the population of cancer survivors, and assessed the value of linked secondary care records for ascertaining COVID-19 at-risk status. Findings On 5 March 2019, 24.4% of the UK population were at risk due to a record of at least one underlying health condition, including 8.3% of school-aged children, 19.6% of working-aged adults, and 66.2% of individuals aged 70 years or more. 7.1% of the population had multimorbidity. The size of the at-risk population was stable over time comparing 2014 to 2019, despite increases in chronic liver disease and diabetes and decreases in chronic kidney disease and current asthma. Separately, 1.6% of the population had a new diagnosis of cancer in the past five years. Interpretation The population at risk of severe COVID-19 (aged ≥70 years, or with an underlying health condition) comprises 18.5 million individuals in the UK, including a considerable proportion of school-aged and working-aged individuals.

scholarly journals Estimation of postpartum depression risk from electronic health records using machine learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guy Amit ◽  
Irena Girshovitz ◽  
Karni Marcus ◽  
Yiye Zhang ◽  
Jyotishman Pathak ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Postpartum Depression ◽  
Electronic Health Records ◽  
Depression Scale ◽  
Well Being ◽  
Accurate Identification ◽  
Health Records ◽  
Electronic Health ◽  
The Uk

Abstract Background Postpartum depression is a widespread disorder, adversely affecting the well-being of mothers and their newborns. We aim to utilize machine learning for predicting risk of postpartum depression (PPD) using primary care electronic health records (EHR) data, and to evaluate the potential value of EHR-based prediction in improving the accuracy of PPD screening and in early identification of women at risk. Methods We analyzed EHR data of 266,544 women from the UK who gave first live birth between 2000 and 2017. We extracted a multitude of socio-demographic and medical variables and constructed a machine learning model that predicts the risk of PPD during the year following childbirth. We evaluated the model’s performance using multiple validation methodologies and measured its accuracy as a stand-alone tool and as an adjunct to the standard questionnaire-based screening by Edinburgh postnatal depression scale (EPDS). Results The prevalence of PPD in the analyzed cohort was 13.4%. Combing EHR-based prediction with EPDS score increased the area under the receiver operator characteristics curve (AUC) from 0.805 to 0.844 and the sensitivity from 0.72 to 0.76, at specificity of 0.80. The AUC of the EHR-based prediction model alone varied from 0.72 to 0.74 and decreased by only 0.01–0.02 when applied as early as before the beginning of pregnancy. Conclusions PPD risk prediction using EHR data may provide a complementary quantitative and objective tool for PPD screening, allowing earlier (pre-pregnancy) and more accurate identification of women at risk, timely interventions and potentially improved outcomes for the mother and child.

Machine learning model to predict mental health crisis from electronic health records

2021 ◽  
Author(s):  
Roger Garriga ◽  
Aleksandar Matić ◽  
Javier Mas ◽  
Semhar Abraha ◽  
Jon Nolan ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
At Risk ◽  
Electronic Health Records ◽  
Learning Model ◽  
Mental Health Crisis ◽  
Health Records ◽  
Health Crisis ◽  
Machine Learning Model ◽  
Electronic Health

Abstract Timely identification of patients who are at risk of mental health crises opens the door for improving the outcomes and for mitigating the burden and costs to the healthcare systems. Due to high prevalence of mental health problems, a manual review of complex patient records to make proactive care decisions is an unsustainable endeavour. We developed a machine learning model that uses Electronic Health Records to continuously identify patients at risk to experience a mental health crisis within the next 28 days. The model achieves an area under the receiver operating characteristic curve of 0.797 and an area under the precision-recall curve of 0.159, predicting crises with a sensitivity of 58% at a specificity of 85%. The usefulness of our model was tested in clinical practice in a 6-month prospective study, where the predictions were considered clinically useful in 64% of cases. This study is the first one to continuously predict the risk of a wide range of mental health crises and to evaluate the usefulness of such predictions in clinical settings.

scholarly journals Finding the Person in Electronic Health Records. A Mixed-methods Analysis of Person-centered Content and Language

2020 ◽  
pp. 1-7
Author(s):  
Birgit Heckemann ◽  
Maryam Chaaya ◽  
Eva Jakobsson Ung ◽  
Daniel S. Olsson ◽  
Sofie Jakobsson
Keyword(s):  
Mixed Methods ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health
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