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Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events

Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events | healthcare technology | Scoop.it
From publichealth.jmir.org - June 16, 2021 8:17 PM

Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.


Objective: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.


Methods: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.


Results: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.


Conclusions: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems.

 

Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether.

 

The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus.

 

Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

 

read the study at https://publichealth.jmir.org/2021/3/e26719

 

nrip's insight:

Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. Using algorithms and/or learning models to extract travel related information from EHR's is not a novel concept but it has come into the spotlight(like most of digital health) in the past 18 months.

 

We should be adding short travel related questionnaires in patient intake forms going forward. The symptoms which trigger this sort of an intake form for a particular patient can change with time, month to month preferably, and be governed by a multi regional , multi national approach. What do you think?

 

 

 

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Shortcomings with the AI Tools and Devices Preventing COVID-19?

Shortcomings with the AI Tools and Devices Preventing COVID-19? | healthcare technology | Scoop.it
From www.altoros.com - May 8, 2021 1:54 AM

Since the start of the pandemic, new technologies have been developed to help reduce the spread of the infection.

Some of the most common safety measures today include measuring a person’s temperature, covering your nose and mouth with a mask, contact tracing, disinfection, and social distancing. Many businesses have adopted various technologies, including those with artificial intelligence (AI) underneath, helping to adhere to the COVID-19 safety measures.

 

As an example, numerous airlines, hotels, subways, shopping malls, and other institutions are already using thermal cameras to measure an individual’s temperature before people are allowed entry. In its turn, public transport in France relies on AI-based surveillance cameras to monitor whether or not people are social-distancing or wearing masks. Another example is requiring the download of contact-tracing apps delivered by governments across the globe.

 

However, there are a number of issues.

 

While many of these solutions help to ensure that COVID-19 prevention practices are observed, many of them have flaws or limits. In this article, we will cover some of the issues creating obstacles for fighting the pandemic.

 

Issue #1. Manual temperature scanning is tricky

Issue #2. Monitoring crowds is even more complex

Issue #3. Contact tracing leads to privacy concerns

Issue #4. UV rays harm eyes and skin

Issue #5. UVC robots are extremely expensive

Issue #6. No integration, no compliance, no transparency

Regardless of the safety measures in place and existing issues, innovations are already playing a vital role in the fight against COVID-19. By improving on existing technology, we can make everyone safer as we all adjust to the new normal.

 

read the details at https://www.altoros.com/blog/whats-wrong-with-ai-tools-and-devices-preventing-covid-19/

 

nrip's insight:

Yes, there are issues with some of the innovations being used. But a faster response is a useful response. I found this post extremely well researched and accurate , and not necessarily negetive. We need criticism of good intentions to make them better. This post does that. These is a valuable list of some shortcomings and some mistakes which will be worked on and improved. Sometimes by changing the system, sometimes by changing the financial model, and sometimes by changing behaviour and mindset.

 

The future of healthcare contains a lot of AI. That bit is true.

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Richard Platt's curator insight, May 10, 2021 11:29 AM

Since the start of the pandemic, new technologies have been developed to help reduce the spread of the infection.

Some of the most common safety measures today include measuring a person’s temperature, covering your nose and mouth with a mask, contact tracing, disinfection, and social distancing. Many businesses have adopted various technologies, including those with artificial intelligence (AI) underneath, helping to adhere to the COVID-19 safety measures.  While there are many AI solutions to help ensure that COVID-19 prevention practices are observed, many of them have flaws or limits. In this article, we will cover some of the issues creating obstacles for fighting the pandemic.   

Issue #1. Manual temperature scanning is tricky
Issue #2. Monitoring crowds is even more complex
Issue #3. Contact tracing leads to privacy concerns
Issue #4. UV rays harm eyes and skin
Issue #5. UVC robots are extremely expensive
Issue #6. No integration, no compliance, no transparency
Regardless of the safety measures in place and existing issues, innovations are already playing a vital role in the fight against COVID-19. By improving on existing technology, we can make everyone safer as we all adjust to the new normal.
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Acceptability of App-Based Contact Tracing for COVID-19

Acceptability of App-Based Contact Tracing for COVID-19 | healthcare technology | Scoop.it
From mhealth.jmir.org - June 12, 2021 7:28 AM

The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs.

 

One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19.

 

Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention.

 

Objective: The objective of this study is to investigate the user

acceptability of a contact-tracing app in five countries hit by the pandemic.


Methods: We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections.

 

We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States and measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries.


Results: We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates.

We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption.


Conclusions:

 

Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.

 

read the study at https://mhealth.jmir.org/2020/8/e19857

 

nrip's insight:

A lot of research and anecdotal evidence shows that mHealth/Mobile App based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app. 

that it can still reduce the number of infections if uptake is moderate is interesting to note.

 

 

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Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review

Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review | healthcare technology | Scoop.it
From publichealth.jmir.org - May 3, 2021 5:38 PM

Web-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality because of swift public health action.

 

Apps accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness.


Objective: The aim of this study is to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community with regard to their design, implementation, and evaluation.

Results: Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised either complex algorithmic or statistical outbreak detection mechanisms or both.

 

We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations.

 

Conclusions: Public health officials designing new or improving existing disease outbreak web-based apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and staff and resources are available to support the operations of the app and conduct rigorous and holistic evaluations.

 

read the study at https://publichealth.jmir.org/2021/4/e24330

 

nrip's insight:

The large scale adoption and constant improvement of these kind of tools - i.e. Tools for Identifying, managing and responding to Infectious Disease Outbreaks in Communities should have started 10 years ago. This is one of my favorite areas of #DigitalHealth. Having been the architect of a number of successful Epidemic Detection and Prediction systems, I feel in this area of Digital Health we still have a long way to go till we reach level where Epidemic Management Teams trust the systems more than their Ears on the ground.

 

But I know that with constant effort, regular additions of modern data paradigms , regular effort and improvement and interdisciplinary cooperation, a point in time where outbreaks can be contained before they occur will come by. Thought that day  is out there in the future ,that  its possibility  alone should drive us forward.

 

To learn about or have a demo of Plus91's Early Warning and Outbreak Detection System which is based on the principles of Syndromic Surveillance and Machine Learning, please contact me via the form with the words "Surveillance Demo" in the message. I promise you it is unlike what you would have seen elsewhere.

 

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