Name: implementation of a real-time psychosis risk detection and alerting system based on electronic health records using cogstack
Uploaded: 2020-05-15T12:30:00.000+00:00
Duration: 7 min 31 s
Description: Watch this Scientific Journal Video about Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack at JoVE.com

This study is funded by and is a direct output of the King&#39;s College London Confidence in Concept award from the Medical Research Council (MRC) (MC_PC_16048) to PFP. RD and AR were supported by: (a) the Maudsley Charity; (b) the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King&#39;s College London; (c) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust; (d) The BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union&#39;s Horizon 2020 research and innovation programme and EFPIA; it is chaired, by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and ESC; and (e) The National Institute for Health Research University College London Hospitals Biomedical Research Centre. These funding bodies had no role in the design of the study, collection and analyses. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

This study was approved by East of England - Cambridgeshire and Hertfordshire Research Ethics Committee (Reference number: 18/EE/0066).
NOTE: We have developed this protocol based on the CogStack platform and the Python programming language. This system requires Docker (more specifically, Docker Compose <a href="https://docs.docker.com/compose/" target="_blank">https://docs.docker.com/compose/</a>), Anaconda Python (<a href="https://www.anaconda.com/distribution/" target="_blank">https://www.anaconda.com/distribution/</a>) and Git (<a href="https://git-scm.com/downloads" target="_blank">https://git-scm.com/downloads</a>) pre-installed on a device. The commands provided in this protocol are based on the Linux environment. In the following, we provide the details of preparing source data from an EHR database, ingesting the data to CogStack platform, and setting up a real-time risk calculation and alerting system for psychosis based on the CogStack platform. Moreover, an online version of the risk calculator was developed to facilitate numeric calculation of the probability of an individual developing psychosis in secondary mental health care on <a href="http://www.psychosis-risk.net/" target="_blank">http://www.psychosis-risk.net</a>.
1. Source data preparation
NOTE: In most use cases, CogStack ingests source data from a specified database view that can combine data from one or more source database tables, where a view is a searchable object in a database that contains the result set of a stored query on the data. The setup of the ingesting view is tailored by the specific use cases and deployment settings of a health record database system. This protocol is developed based on a psychosis risk calculator developed and externally validated twice by Fusar-Poli et al.14,21 and as part of a pilot implementation feasibility study6. The protocol is based on an EHR database deployed with Microsoft SQL Server 2014.
<ol>
	<li>Create a view object (called &#34;vwPsychosisBase&#34; in this protocol) in an existing EHR database system to join necessary information of patients for psychosis risk calculation and alerting. Make sure that this view includes all patients receiving a first primary diagnosis of non-organic and non-psychotic mental disorder (recorded by the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10]), as defined in the original model14,21.</li>
	<li>Ensure that each record in the view involves three types of patient information: 1) the first primary diagnosis of a patient in the EHR system, including ICD-10 diagnosis index (diagnoses were clustered together into the following ten clusters: acute and transient psychotic disorders, anxiety disorders, bipolar mood disorders, childhood and adolescent onset disorders, developmental disorders, nonbipolar mood disorders, mental retardation, personality disorders, physiological syndromes, substance use disorders) and diagnosis date; 2) a patient&#39;s demographic data, including gender, ethnicity and date of birth; and 3) the most recent contact information of care team for a patient, such as details of general practice (GP), consultants and care coordinators. The first two types of information are vital for the psychosis risk calculator14,21, and the third type of information is to enable timely risk alerting.</li>
	<li>Make sure that each record in the view has a unique identifier (e.g., &#34;patient_id&#34; used in this protocol).</li>
	<li>Select the last update timestamps of all source information related to a record in the view (e.g., the last update times of a patient&#39;s demographic information and the patient&#39;s first primary diagnosis information), and choose the latest timestamp as the last update date and time for the record in the view (denoted as &#34;etl_updated_dttm&#34; in this protocol). The last update date and time of a record allows CogStack to synchronize updates in the database, such as new and updated records.</li>
</ol>
2. Data ingestion
<ol>
	<li>Download or clone the code repository from Github (<a href="https://github.com/cogstack-slam/psychosis" target="_blank">https://github.com/cogstack-slam/psychosis</a>) or by typing &#34;git clone <a href="https://github.com/cogstack-slam/psychosis.git" target="_blank">https://github.com/cogstack-slam/psychosis.git</a>&#34; in a terminal window. The downloaded folder contains the code for psychosis risk calculation and configuration files for deploying a CogStack instance.</li>
	<li>Go to the &#34;cogstack_deploy/cogstack/&#34; directory and modify &#34;psychosis.properties&#34; to configure CogStack Pipeline for data ingestion. Modify the settings of section &#34;SOURCE: DB CONFIGURATIONS&#34; based on the EHR database setup, including specifying the IP address of the database server, database name, database username and password. Modify the view name (i.e. &#34;vwPsychosisBase&#34;) and field names (e.g., &#34;patient_id&#34; and &#34;etl_updated_dttm&#34;) if necessary. In case of error in configuring this file, follow the instructions at <a href="https://cogstack.atlassian.net/wiki/spaces/COGDOC/pages/38043684/Quickstart" target="_blank">https://cogstack.atlassian.net/wiki/spaces/COGDOC/pages/38043684/Quickstart</a>.</li>
	<li>Go to the &#34;cogstack_deploy/common/elasticsearch/config/&#34; directory and modify the section &#34;xpack.notification.email.account&#34; in the &#34;elasticsearch.yml&#34; file to configure an Email address for sending alerts. A detailed instruction for Email configuration can be found on <a href="https://www.elastic.co/guide/en/kibana/6.4/watcher-create-threshold-alert.html" target="_blank">https://www.elastic.co/guide/en/kibana/6.4/watcher-create-threshold-alert.html</a>.</li>
	<li>Go to the &#34;cogstack_deploy/&#34; directory and type &#34;docker-compose up&#34; to run the CogStack platform. Execute this command with root access. If the process is completed successfully, there will be printed status logs of the currently running services, including CogStack Pipeline, Elasticsearch and Kibana, in the terminal. As a result, all data and updates in the source database view will be timely ingested to an Elasticsearch index called &#34;psychosis_base&#34; in the CogStack platform.</li>
	<li>Open a web browser and access Kibana user interface by typing &#34;http://localhost:5601/&#34; (or replacing &#34;localhost&#34; with a specific IP address of the server running the CogStack platform). For the first time accessing Kibana, click the Management tab and Index Patterns tab to specify an Elasticsearch index that one wants to access with Kinaba. Type &#34;psychosis_base&#34; in the &#34;Index pattern&#34; field and click Next step. Select &#34;etl_updated_dttm&#34; for the &#34;Time Filter&#34; field name and click Create index pattern to add the &#34;psychosis_base&#34; index pattern for Kinana.</li>
	<li>Once Kibana is connected to the Elasticsearch index (i.e., &#34;psychosis_base&#34;), search and browse the source data interactively through the &#34;Discover&#34; page. Kibana allows non-technical users to search for both structured metadata and free text. Detailed instructions of using &#34;Discover&#34; are available on <a href="https://www.elastic.co/guide/en/kibana/6.4/discover.html" target="_blank">https://www.elastic.co/guide/en/kibana/6.4/discover.html</a>.</li>
</ol>
3. Risk calculation
<ol>
	<li>Open a new terminal window and go to the &#34;psychosis/&#34; directory. Install all required Python packages (including &#34;elasticsearch&#34;, &#34;elasticsearch_dsl&#34;, &#34;pandas&#34; and &#34;numpy&#34;) used in the risk calculator by typing &#34;conda install package-name&#34; or &#34;pip install package-name&#34; in the terminal.</li>
	<li>Type &#34;python risk_calculator.py&#34; to run the psychosis risk calculator. If the process is completed successfully, logs of the risk calculation will be printed in the terminal and the risk results will be stored in a new Elasticsearch index called &#34;psychosis_risk&#34; within the CogStack platform.</li>
	<li>Check the risk results by using the Kibana interface. Similar to Steps 2.5 and 2.6, add a new index pattern &#34;psychosis_risk&#34; to connect Kinbana with the &#34;psychosis_risk&#34; index, and explore the risk results through the &#34;Discover&#34; page. To facilitate identifying new patients at-risk, use &#34;first_primary_diagnosis_date&#34; as the &#34;Time Filter&#34; field in building the &#34;psychosis_risk&#34; index. When exploring data in the &#34;Discover&#34; page, make sure that the index pattern &#34;psychosis_risk&#34; is selected.</li>
</ol>
4. Data visualization
<ol>
	<li>In addition to searching and accessing individual-level information via the &#34;Discover&#34; page in Kibana, one can build visualizations and dashboards to obtain an overview of characteristics for the whole population of at-risk patients. To do this, click on Visualize in the side navigation of Kibana. Then, click the Create new visualization button and choose a visualization type (e.g., pie and line charts). Select &#34;psychosis_risk&#34; as the index that one wants to visualize through Kibana. By default, visualizations will include all records/patients in the &#34;psychosis_risk&#34; index. Detailed instructions of building Kibana visualizations are available on <a href="https://www.elastic.co/guide/en/kibana/6.4/visualize.html" target="_blank">https://www.elastic.co/guide/en/kibana/6.4/visualize.html</a>.</li>
	<li>To select a specific subset of data for visualization, add a &#34;filter&#34;. For example, selecting a filter filed as &#34;h_2_year&#34;, choosing an operator as &#34;is not between&#34; and setting values from &#34;0.0&#34; to &#34;0.05&#34; will only include patients whose risk of psychosis in 2 years are higher than 0.05.</li>
	<li>Once individual visualizations are built, click on Dashboard in the side navigation of Kibana to create a dashboard that displays a set of related visualizations together. Click Create new dashboard and the Add button to create a new dashboard panel. Click visualizations that one wants to show within the new dashboard panel. Click Save and type a title to save the panel. Instructions on building Kibana dashboards are available at <a href="https://www.elastic.co/guide/en/kibana/6.4/dashboard.html" target="_blank">https://www.elastic.co/guide/en/kibana/6.4/dashboard.html</a>.</li>
</ol>
5. Risk Alerting
<ol>
	<li>Click on Management in the side navigation of Kibana and then click Watcher under Elasticsearch to create alerting for clinicians when patients were at-risk of psychosis. If the Watcher button is not visible, click License Management and click Start trial or Update license.</li>
	<li>Click Create advanced watch to set up a new Watcher. Type an &#34;ID&#34; and &#34;Name&#34;. Delete the content of &#34;Watch JSON&#34; section and copy the content in the &#34;watcher.json&#34; file in the &#34;psychosis&#34; directory to the &#34;Watch JSON&#34; section. This watcher will send alerting Email to &#34;clinician@nhs.uk&#34; (which can be replaced with the Email address where one wants to send alerts) from &#34;username@nhs.uk&#34; (which was set in Step 2.3) if there are one or more patients whose risk of psychosis in 2 years are higher than 0.05 (a tentative threshold for feasibility testing) in every 24 hours.</li>
	<li>Before saving the Watcher, click Simulate to test the Watcher execution. If the Watcher is set successfully, one will see the simulation output printed. In case of error in the settings, follow the instructions on <a href="https://www.elastic.co/guide/en/elastic-stack-overview/6.4/watcher-getting-started.html" target="_blank">https://www.elastic.co/guide/en/elastic-stack-overview/6.4/watcher-getting-started.html</a>.</li>
	<li>To stop a Watcher, permanently delete it or temporarily deactivate it from the &#34;Status&#34; page of the Watcher.</li>
</ol>

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The authors have nothing to disclose.

We have demonstrated the first EHR implementation of a real-time psychosis risk detection and alerting system based on CogStack, an open source information retrieval and extraction platform. Following this approach, one can transform and ingest a large set of clinical data in various formats, including structured and unstructured information, into a CogStack instance, so as to enable full-text search, interactive analyses and visualization of data, as well as real-time alerting to clinicians of patients that are at-risk of psychosis. Although the original psychosis risk calculator has been validated in pilot studies across several NHS Trusts, albeit using retrospective patient records6,14,21, this experimental design provides the first evidence base that this risk calculator can be replicated and deployed for use in real time. This approach allows the automatic delivery of prognostic results to clinicians through existing clinical notification channels, such as Email, in real time. This clearly demonstrates the technical feasibility for conducting a large-scale effectiveness trial to evaluate the ultimate clinical utility of this risk calculator in the real world.
This protocol is empirically innovative, as there does not exist a similar risk detection and alerting system for psychosis. Moreover, this protocol has high generalizability in clinical use, particularly because of the unique strengths of our approach. From a theoretical perspective, we used a risk prediction model that was developed based on a large retrospective de-identified cohort from the SLaM NHS Trust. SLaM provides secondary mental health care to a population of 1.36 million individuals in South London and has one of the highest recorded rates of psychosis in the world. This large cohort, which has high diversity in sociodemographic and diagnostic characteristics, allows us to develop a risk prediction model that is unlikely to be biased towards a population with specific characteristics. This is supported by evidence that the prognostic accuracy of this risk calculator has already been replicated twice in two different databases14,21, including one outside of SLaM. Another theoretical strength of this risk model is that basic demographic and clinical diagnosis information were used as predictors. Such information is ubiquitous in electronic clinical data and in fact missing data for these predictors have been shown to be relatively rare in our previous studies14,21. The high availability of information for building predictors makes it possible to run the risk calculator over a large number of patient samples across different secondary mental health care sectors. In addition, the risk calculator is a general algorithm which is suitable for all individuals at-risk of developing psychosis in secondary mental health care, regardless of individuals&#39; ages. That is, this calculator is not only suitable for the 15-35 age range of peak psychosis risk16, but also for those outside of this range, showing a high degree of generalizability.
From a practical perspective, both the risk calculator and the CogStack platform are light-weight and open-source services that do not involve resource-heavy techniques or costly infrastructure. Such a low-cost and easy-to-deploy platform can reduce the barriers to its adoption in real-world clinical settings. Also, our solution overcomes the main implementation barrier: risk estimation systems provide little value unless they are used by clinicians in day-to-day practice25. Specifically, our approach accesses data from the EHR, performs analyses independent of an electronic medical record system and can send analysis results back to clinicians through existing notification channels. This method does not require that the business logic in pre-existing systems be modified and can work as a standalone service to support and extend existing clinical decision support systems. Thus, the protocol has high compatibility with pre-existing clinical systems and can be easily integrated into routine clinical practice. Moreover, the protocol provides user-friendly interfaces for searching, analyzing and visualizing of clinical data, which make it easy for clinicians to interpret and explore the risk results.
This protocol also has its limitations. First, the effectiveness of this protocol has not been evaluated in routine clinical practice. This study focused on technical feasibility tests of implementing a real-time psychosis risk detection and alerting system in a local EHR. To further evaluate the effectiveness of this system in routine clinical practice, future large scale randomized controlled trials are needed6. A second limitation is that the predictions of risk scores in this protocol were made based on the first primary diagnoses, which are static data collected at a single snapshot. However, the CHR-P symptoms are intrinsically evolving over time. A dynamic version of psychosis risk calculator, in which prediction models can be dynamically updated to reflect the changes, has been developed recently26. Future work will focus on integrating this dynamic calculator in the current protocol.
The most critical step in this approach was identifying EHR data that were used for extract predictors in the risk calculator. This may also involve creating data element mappings, when an EHR system used a data model different from that used in this protocol, such as distinct coding systems for patients&#39; ethnic groups. We have open-sourced all the code and mapping definitions online (<a href="https://github.com/cogstack-slam/psychosis" target="_blank">https://github.com/cogstack-slam/psychosis</a>). Based on these materials, one would be able to replicate the deployment or adjust the calculator depending on one&#39;s own circumstance. Another critical step was creating a database view for data ingestion in CogStack. Since relational join operations (i.e., combining columns from one or more database tables) in Elasticsearch can lead to high computational cost, we conducted these join operations in the EHR database by creating a database view. This view combined all information that was needed to extract predictors in the risk calculator, and two vital fields that were used by CogStack pipelines for data partitioning in data ingestion. The first field is a unique primary key for each record in the view (&#34;patient_id&#34; used this protocol) and the second is a timestamp when a record was modified most recently. If these two fields were not set properly, CogStack might not synchronize data updates in an EHR database timely. Detailed instructions for troubleshooting issues on CogStack data ingestion are available on <a href="https://cogstack.atlassian.net/wiki/spaces/COGDOC/overview" target="_blank">https://cogstack.atlassian.net/wiki/spaces/COGDOC/overview</a> and <a href="https://github.com/CogStack/CogStack-Pipeline" target="_blank">https://github.com/CogStack/CogStack-Pipeline</a>.
This protocol is highly transportable and can be easily deployed in NHS Trusts that have a CRIS or CogStack platform. So far, the CRIS platform-including the consenting procedures-has been fully described elsewhere and is under expansion across 12 NHS Trusts in the UK, harnessing over 2 million deidentified patient records (<a href="https://crisnetwork.co/" target="_blank">https://crisnetwork.co/</a>). Similarly, the CogStack platform has been deployed not only in SLaM, but also other NHS Trusts across the UK such as University College London Hospitals (UCLH), King&#39;s College Hospital (KCH), Guy&#39;s and St Thomas&#39; (GSTT), and Mersey Care NHS Trusts. Those Trusts without such as platform can use an online version of risk calculator (<a href="http://psychosis-risk.net/" target="_blank">http://psychosis-risk.net</a>), or build this protocol from scratch based on this manuscript and our online documents. Although this protocol is developed for psychosis risk detection, the architectural design of this protocol is not tied-in to this specific use case. The protocol is flexible enough to allow for reconfiguration and repurposing of the real-time monitoring and alerting components for other risk measurement areas, such as adverse drug reactions, thereby allowing clinicians to timely take action to improve patient care, safety and experience.

Psychotic disorders are serious mental health illnesses that lead to difficulties in distinguishing between the internal experience of the mind and the external reality of the environment1, as well as a higher than average risk of self-harm and suicide2. Under standard care, these disorders result in major public health impact with a significant health and economic burden on individuals, families and societies worldwide3. Early interventions in psychosis can improve outcomes of this mental disorder4. In particular, detection, prognostic assessment and preventive treatment of individuals who are at clinical high risk for developing psychosis (CHR-P)5 provides a unique potential to alter the course of the disorder, thereby improving the quality of life for many people and their families3,6. CHR-P individuals are help-seeking young people presenting with attenuated symptoms and functional impairment7: their risk of developing psychosis is 20% at 2-years8 but it is higher in some specific subgroups9,10. Despite some substantial advancements, the impact of preventive approaches in routine clinical practice is limited by the ability to detect most individuals who are at-risk11. Current detection methods are based on help-seeking behaviors and referrals on suspicion of psychosis risk; these methods are highly inefficient in handling a large number of samples11. Thus, the scalability of current detection methods to the vast majority of the at-risk population is quite limited12. In fact, only 5% (standalone specialized early detection services) to 12% (youth mental health services) of individuals at-risk of developing a first psychotic disorder can be detected at the time of their at-risk stage by the current detection strategies6.
To extend the clinical benefits of the preventive approaches in a larger number of at-risk individuals, we developed an automated, lifespan-inclusive (i.e., across all ages), transdiagnostic (i.e., across different diagnoses)13, clinically-based individualized risk calculator, which can detect individuals at-risk of psychosis in secondary mental health care at scale, beyond those meeting CHR-P criteria14. This risk calculator used a Cox proportional hazard model to predict the risk of developing a psychotic disorder over six years from five routinely collected clinical variables selected a priori, in line with methodological guidelines15: age, gender, ethnicity, age-by-gender and primary index diagnosis. These clinical variables were selected based on a priori knowledge obtained from meta analyses16,17, as recommended by the state-of-the-art methodological guidelines15. The number of predictors is limited to preserve the Event Per Variable ratio and minimize overfitting biases; including too many variables without a priori filter leads to overfitting problems and poor prognostic accuracy18. The method used to develop this model provides similar prognostic accuracy to automatic machine learning methods18. Parameters of the Cox model were estimated based on a retrospective de-identified cohort from the South London and Maudsley National Health Service Foundation Trust (SLaM)19. SLaM is a National Health Service (NHS) mental health trust that provides secondary mental health care to a population of 1.36 million individuals in South London (Lambeth, Southwark, Lewisham and Croydon boroughs), and has one of the highest recorded rates of psychosis in the world20. All data used in the model development were extracted from the Clinical Record Interactive Search (CRIS) platform, a digital case register system, which provides researchers with retrospective access and analysis of anonymized clinical records19. The clinical information in CRIS is extracted from a bespoke Electronic Health Record (EHR) system, at SLaM, called electronic Patient Journey System (ePJS). SLaM is paper-free and ePJS represents the standard data collection platform for clinical routine. Thus, the transdiagnostic risk calculator leverages EHRs and has the potential to automatically screen large EHRs of patients accessing secondary mental healthcare, to detect those who may be at-risk of psychosis. The algorithm of this transdiagnostic risk calculator has been published previously6,14,21. The transdiagnostic risk calculator has been externally validated in two NHS Foundation Trusts14,21 and optimized22, demonstrating its adequate prognostic performance and generalizability across different populations.
According to methodological guidelines on the development of a risk prediction model15,23, the next step after model development and validation is to implement the prediction model in routine clinical practice. Implementation studies are usually preceded by pilot or feasibility studies that address potential pragmatic limitations associated with the use of risk algorithms in clinical practice. For example, required data for running a calculator, such as age, gender and ethnicity, may not be available at the date of diagnosis or updated later. Effective methods for handling missing data and synchronizing frequent updates in real-time data streams should be considered to obtain the most reliable prediction results in an implementation. Furthermore, since the initial development of the risk calculator was based on retrospective cohort data, it is not known whether it can be used in a real-time data stream that is typical of a real-world clinical setting. Another challenge is ensuring that relevant clinicians receive the recommendations generated by the risk calculator within an appropriate time frame and within a shared and accepted communication pathway.
To overcome these limitations, we have completed a feasibility implementation study employing the individualized transdiagnostic risk calculator. The study included two phases: an in vitro phase that was conducted using data from the local EHR, without contacting clinicians or patients, and an in vivo phase, which involved direct contact with clinicians. The in vitro phase had two manifold aims: (i) to address implementation barriers according to the Consolidated Framework for Implementation Research (CFIR)27 and (ii) to integrate the transdiagnostic risk calculator into the local EHR. Implementation barriers included the communication of risk outcomes to clinicians. In SLaM, all patients are invited to register for Consent for Contact (C4C), which indicates their willingness to be contacted for research, without affecting the quality of care. This reduces the ethical issues relating to contacting patients. Further to this, working groups with clinicians aided tailoring of how this information was communicated. During the in vivo phase (May 14th 2018 to April 29th 2019), all individuals (i) older than 14 years (ii) who were accessing any SLaM service (boroughs of Lambeth, Southwark, Lewisham, Croydon), (iii) receiving a first ICD-10 index primary diagnosis of any non-organic, non-psychotic mental disorder (with the exception of Acute and Transient Psychotic Disorders; ATPD), or a CHR-P designation and (iv) with existing contact details were deemed eligible. During the in vivo phase, new patients accessing SLaM each week were automatically screened for their psychosis risk, and those with having a risk greater than a certain threshold were detected. The research team then contacted the patients&#39; responsible clinicians to discuss further recommendations and eventually suggest a further face to face assessment6. If those assessed were considered to meet CHR-P criteria, they were referred to specialist CHR-P services, such as Outreach and Support in South London (OASIS)28. This would result in improved detection of individuals prior to the onset of a psychotic disorder and provide a significant opportunity for altering the course of the disorder. Crucially, this feasibility study involved the full integration of the calculator into the local EHR system, which is the topic of the current article. The full protocol of this feasibility study, including an overview of the plan for evaluating the proposed research, details on managing data security and ethical issues, has been presented in our previous work6. The current article, as a part of the feasibility study6, selectively focuses on presenting the technical implementation of a real-time psychosis risk detection and alerting system based on the local EHR data. More specifically, the aim of this study is to investigate the technical feasibility of this risk calculator in timely detecting at-risk patients as soon as they access a secondary mental healthcare service. The full results of the feasibility study, in terms of clinicians&#39; adherence to the recommendations made by the risk calculator, will be presented separately. A comprehensive evaluation of the effectiveness of the proposed research, which requires randomized designs, is outside the scope of the current research program. To our best knowledge, this is the first method describing the implementation of a risk calculator based on live EHR data for early detection of psychosis.
Our approach to psychosis risk detection and alerting takes advantage of the CogStack platform. The CogStack platform is a lightweight, distributed, and fault-tolerant information retrieval and text-extraction platform24. This platform consists of three key components: 1) the CogStack Pipeline that uses the Java Spring Batch framework to ingest and synchronize data from a pre-defined data source (both structured and unstructured EHR data in multiple formats such as Word, PDF files and images) to a predefined data sink in real time; 2) Elasticsearch, a search engine allowing for storage and querying of the full text of EHR data, as well as providing various application programming interfaces (APIs) to embed advanced analytics into the engine; and 3) Kibana, an interactive, web-based user interface that allows users to query data in Elasticsearch, build visualization dashboards and set alerting on anomalies or other patterns of interest from data. Moreover, CogStack incorporates the ability to alert clinicians to potential problems by Email and SMS (text), allowing clinicians to receive timely notifications about at-risk patients reported by the risk calculator.
We present a model of psychosis risk detection and alerting based on ePJS at SLaM, leveraging the CogStack platform. Compared with the CRIS platform that provides a mechanism for retrospective access to de-identified health records from ePJS on a weekly basis19, the CogStack platform at SLaM enables access to an identifiable EHR in real time, bringing the alerting closer to the point-of-care and the risk prediction in a prospective design, although both the CRIS and CogStack platforms use data sourced from ePJS in SLaM. In the section that follows, we provide details of the key steps in our approach, including preparing source data from the EHR, ingesting the source data into the CogStack platform to enable full-text search via Elasticsearch, running the psychosis risk calculator using a Python daemon thread, and setting interactive visualizations and real-time risk alerting via the Kibana user interface. Any researcher who aims to build a real-time risk detection and alerting system based on EHR data can follow the approach and its reference implementation. As we shall elaborate below, the proposed method exploits open-source, lightweight techniques with high flexibility and portability. This enables the risk calculator to be run in various locations and shows a high applicability to other risk estimation algorithms. Moreover, the method works as a straightforward approach to enhance the risk detection and alerting functionalities of an EHR embedded in a general healthcare system.

<table><tbody><tr><td>CogStack-Pipeline</td><td>King&#039;s College London</td><td></td><td>Open source software</td></tr><tr><td>Elasticsearch</td><td>Elastic NV</td><td></td><td>Open Source Search &amp;amp; Analytics</td></tr><tr><td>Kibana</td><td>Elastic NV</td><td></td><td>Open source data visualization plugin for Elasticsearch</td></tr><tr><td>Python packages (&quot;elasticsearch&quot;, &quot;elasticsearch_dsl&quot;, &quot;pandas&quot; and &quot;numpy&quot;)</td><td>Open source community</td><td></td><td>Open source packages</td></tr></tbody></table>

implementation of a real-time psychosis risk detection and alerting system based on electronic health records using cogstack

Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful system for supporting the early detection of individuals at-risk of psychosis at a large scale, by leveraging electronic health records (EHRs). This risk calculator has been externally validated twice and is undergoing feasibility testing for clinical implementation. Integration of this risk calculator in clinical routine should be facilitated by prospective feasibility studies, which are required to address pragmatic challenges, such as missing data, and the usability of this risk calculator in a real-world and routine clinical setting. Here, we present an approach for a prospective implementation of a real-time psychosis risk detection and alerting service in a real-world EHR system. This method leverages the CogStack platform, which is an open-source, lightweight, and distributed information retrieval and text extraction system. The CogStack platform incorporates a set of services that allow for full-text search of clinical data, lifespan-inclusive, real-time calculation of psychosis risk, early risk-alerting to clinicians, and the visual monitoring of patients over time. Our method includes: 1) ingestion and synchronization of data from multiple sources into the CogStack platform, 2) implementation of a risk calculator, whose algorithm was previously developed and validated, for timely computation of a patient&#39;s risk of psychosis, 3) creation of interactive visualizations and dashboards to monitor patients&#39; health status over time, and 4) building automated alerting systems to ensure that clinicians are notified of patients at-risk, so that appropriate actions can be pursued. This is the first ever study that has developed and implemented a similar detection and alerting system in clinical routine for early detection of psychosis.

In this section, we present implementation results focusing in practicality in handling live clinical data streams elaborated through the risk calculator and facilitating timely delivery of prognostic results to clinicians. Evaluations of the clinical utility of the system, such as the adherence of clinicians to the recommendations made by the risk calculator, will be presented in a separate report when complete.
Ingestion of source data 
We deployed the psychosis risk calculation and alerting system based on a replica database of ePJS in SLaM. This replica database synchronizes the live data from ePJS every 10 minutes. A database view combining patients&#39; information for psychosis risk calculation was built in this replica database, where each record contains information for a patient. All records in this view were ingested into the CogStack platform in real time (approximately 0.6 microsecond per record in a virtual machine with 8-core CPU and 16GB RAM). Until 13 July 2019 when this manuscript was prepared, all the records of 202,289 patients who received a first index diagnosis of non-organic and non-psychotic mental disorder in SLaM were ingested into CogStack for psychosis risk calculation, stored in the &#34;psychosis_base&#34; Elasticsearch index. Figure 1 shows the number of records ingested into CogStack over time, in chronological order based on the last update date of a record. By comparing the numbers and content of records in the database and the Elasticsearch index, no missing and discrepant data were found, which confirms the reliability of CogStack Pipeline in data ingestion and synchronization.
Validation of risk results 
To validate the implementation of the psychosis risk detector in this protocol, we compared at-risk patients detected by CogStack (called &#34;CogStack version&#34;) with those detected by the original risk calculator based on CRIS (called &#34;CRIS version&#34;). Since there were no thresholds developed to screen an at-risk patient6,14,21, we here used a tentative threshold of 5% for the risk of psychosis in two years. Note that this tentative threshold is merely to test whether the system can pragmatically work in the NHS and is susceptible to change with future research. The actual threshold for an optimal detection of at-risk individuals will need to be identified in future large-scale studies. Specifically, we first retrieved all patients who had a risk for psychosis above the threshold in the CRIS version (the number of patients N=169). All these patients received a first index diagnosis of non-organic and non-psychotic mental disorder in SLaM from May 14th 2018 to April 29th 2019. By filtering patients who were diagnosed in the same time period, we then retrieved N=170 patients whose risk for psychosis in 2 years were higher than 0.05 in the CogStack version. Finally, we compared the difference between the two sets of patients, where the total number of unique patients in the two sets are N=173. We found that 161 patients (accounting for 93% of 173 patients) had the same scores in both versions. The high degree of agreement confirms the validity of this CogStack-based protocol in generating risk scores.
There were 12 patients having different risk scores in the two versions. By inspecting patients&#39; EHRs, we found that this difference was because data for these patients were updated after the risk scores were calculated in the CRIS version. Specifically, although predictors used in the risk calculator, such as date of birth, gender and self-assigned ethnicity, were static variables, some patients&#39; health records had a missing or default value for a variable (e.g., an unknown ethnicity) at an earlier stage and these variables were entered or updated at a later stage. This can lead to different risk scores at two different stages. Similarly, the first primary index diagnoses of some patients were invalidated after an initial risk score was calculated based on these diagnoses. In this case, the risk calculator will look for the next valid primary diagnosis for such patient and re-calculate a risk score. The updated risk score can also differ from the initial one. As the original risk calculator was developed based on retrospective data in CRIS for research use, the original calculator pipelines did not synchronize these updates in EHR data and refresh the risk scores in a timely manner. In contrast, a patient&#39;s risk score will be re-calculated in the CogStack version if any source data of the patient is updated, which allows this CogStack-based calculator to provide the most reliable and up-to-date risk scores for patients. These results strongly highlight the reliability of risk scores in this protocol.
Result visualization and risk alerting 
To demonstrate the capabilities of CogStack in data visualization, we built a dashboard for information about patients at-risk of psychosis. As used before for feasibility testing, we selected those who have a risk of psychosis in two years higher than 5% as at-risk patients. Figure 2 shows the visualizations of characteristics for patients at-risk of psychosis, including patients&#39; ethnicities, genders, ages and categories of diagnoses. Apart from visualizing risk results via Web interfaces (e.g., Kibana), this protocol allows risk alerts to be sent to users or clinicians through other notification channels such as Email. Figure 3 shows the interface for setting a risk alerting service by using the Watch component in Kibana. Once this service is configured successfully, users can receive an Email notification if there were one or more patients whose risk of psychosis in two years are higher than 5%. Figure 4 shows an example of these Email notifications, which reports the numbers of patients at-risk and these patients&#39; boroughs. Since more work is needed to tailor how the predicted psychosis risk scores are communicated, we have not sent risk notifications directly to clinicians. For testing the technological feasibility, all notifications in this study were sent from a technical researcher (T.W.) to a clinical researcher (D.O.) via the SLaM&#39;s email system within a secure network. Only an aggregated statistic of patient information was included in a notification; no any personally identifiable information was included.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60794/60794fig1.jpg" /> 
Figure 1: Source data ingested into CogStack. There are 202,289 records in total ingested into the &#34;psychosis_base&#34; Elasticsearch index until 13 July 2019, and the histogram shows the numbers of records ingested over time, ordered by the last update data time of a record. One can also query both structured and unstructured information, and obtain search hits that match the query in this page. <a href="https://www.jove.com/files/ftp_upload/60794/60794fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60794/60794fig2.jpg" /> 
Figure 2: Dashboard of characteristics of patients at-risk of psychosis (i.e., the risk of psychosis in 2 years higher than 0.05). (a) Distribution of ethnicities for patients at-risk, where outer pies are the subcategories of an ethnicity category in inner pies. (b) distribution of patients&#39; gender, (c) distribution of patients&#39; ages at diagnosis and (d) number of patients per diagnosis group. <a href="https://www.jove.com/files/ftp_upload/60794/60794fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60794/60794fig3.jpg" /> 
Figure 3: Setting and simulating Watch for risk alerting. <a href="https://www.jove.com/files/ftp_upload/60794/60794fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60794/60794fig4.jpg" /> 
Figure 4: An example of risk alerting Email. The numbers of patients at-risk of psychosis in each Clinical Commissioning Groups (CCG) are reported in parentheses. <a href="https://www.jove.com/files/ftp_upload/60794/60794fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack at JoVE.com

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

We demonstrate how to deploy a real-time psychosis risk calculation and alerting system based on CogStack, an information retrieval and extraction platform for electronic health records.

Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful ...

implementation-real-time-psychosis-risk-detection-alerting-system

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JoVE Journal

Behavior

7.0K Views.  King's College London. De impact van psychosepreventie wordt beperkt door hoe effectief we risicopersonen kunnen detecteren. Dit protocol presenteert een nieuwe aanpak voor de implementatie van een psychose risicodetectie en waarschuwingsdienst in een echte wereld elektronische gezondheidsdossier systeem. Het belangrijkste voordeel van deze klinisch gebaseerde geïndividualiseerde risicocalculator is dat het mogelijk maakt voor de automatische detectie van personen die risico lopen op psychose op grote schaal met b...

Video: Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches for emotional regulation and optimizing compensatory reserve mechanisms. This protocol is designed to not only teach mind-body awareness but also to provide feedback utilizing physiological data and survey results. The primary findings of this study showed that heart coherence and alpha variables were significantly correlated after four weeks of the breath-based meditation stress protocol. Meditation and rhythmic breathing produced significant increases in alpha brain activity. These brain physiological responses conformed to the Pleth Variability Index (PVI) changes, suggesting the ability of the human body to enter into a meditative state and effectively manage stress. When assessed after four weeks of daily practicing the techniques employed in the stress management protocol, based on the Five Facet Mindfulness Questionnaire, subjects improved in applying mindfulness skills. The overall mindfulness score, Pleth variability index (PVI), and perfusion index (PI) increased after the 4-week intervention period. Results from electroencephalography (brain waves) were consistent with a meditative state during the post-study follow-up session. This provides evidence that wearable devices are feasible for data collection during a breath-based stress management intervention. This protocol can be easily and efficiently implemented into any study design in which physiological data are desired in a non-laboratory-based setting.

Stress is an unavoidable and persistent component of life and holistic approaches for its management are being considered. A standardized methodology was created to demonstrate the feasibility of a breath-based stress management protocol that can be used with commercially available portable technology.

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches ...

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., &lt;1 cm/&lt;1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning1, auditory processing2 and visual-spatial attention3. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function1-5. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user's intentions to enhance or improve communication6 and control7. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform8,9 and the SIGFRIED10 method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation.
Prerequisites and Planning:
Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient's brain, and on relevant factors of the patient's history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure.
In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient's performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital's institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient's endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort.
At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient's eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5th Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment.
Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software8,9. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator.
A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient's electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS).
Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping10.
The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

We present a method for collecting electrocorticographic signals for research purposes from humans who are undergoing invasive epilepsy monitoring. We show how to use the BCI2000 software platform for data collection, signal processing and stimulus presentation. Specifically, we demonstrate SIGFRIED, a BCI2000-based tool for real-time functional brain mapping.

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), ...

Neuroscience

Assessment and Communication for People with Disorders of Consciousness

In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the level of consciousness of people unable to provide motor response and, in a second stage, establishing a communication channel for these people that enables them to answer questions with either &#39;yes&#39; or &#39;no&#39;. The suite of paradigms is designed to test basic responses in the first step and to continue to more comprehensive tasks if the first tests are successful. The latter tasks require more cognitive functions, but they could provide communication, which is not possible with the basic tests. All assessment tests produce accuracy plots that show whether the algorithms were able to detect the patient&#39;s brain&#39;s response to the given tasks. If the accuracy level is beyond the significance level, we assume that the subject understood the task and was able to follow the sequence of commands presented via earphones to the subject. The tasks require users to concentrate on certain stimuli or to imagine moving either the left or right hand. All tasks are designed around the assumption that the user is unable to use the visual modality, and thus, all stimuli presented to the user (including instructions, cues, and feedback) are auditory or tactile.

With this experiment, one might be able to detect consciousness in people with disorders of consciousness. Furthermore, the approach can create a simple communication channel that enables people to give simple YES/NO answers to questions.

In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the ...

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

The evidence-informed standardization of care along disease lines is recommended to improve outcomes and reduce healthcare costs. The aim of this project is to 1) describe the development and implementation of the Concussion Carepath, 2) demonstrate the process of integrating technology in the form of a mobile application to enable the carepath and guide clinical decision-making, and 3) present data on the utility of the C3 app in facilitating decision-making throughout the injury recovery process. A multi-disciplinary team of experts in concussion care was formed to develop an evidence-informed algorithm, outlining best practices for the clinical management of concussion along three phases of recovery &#8211; acute, subacute, and post-concussive. A custom mobile application, the Cleveland Clinic Concussion (C3) app was developed and validated to provide a platform for the systematic collection of objective, biomechanical outcomes and to provide guidance in clinical decision-making in the field and clinical environments. The Cleveland Clinic Concussion app included an electronic incident report, assessment modules to measure important aspects of cognitive and motor function, and a return to play module to systematically document the six phases of post-injury rehabilitation. The assessment modules served as qualifiers within the carepath algorithm, driving referral for specialty services as indicated. Overall, the carepath coupled with the C3 app functioned in unison to facilitate communication among the interdisciplinary team, prevent stagnant care, and drive patients to the right provider at the right time for efficient and effective clinical management.

A multidisciplinary carepath to standardize disparate concussion care was developed and implemented at the Cleveland Clinic. The Cleveland Clinic Concussion (C3) mobile application was used to enable the carepath through biomechanical outcomes characterizing cognitive and motor function. Patient outcomes improved while cost of care was reduced following implementation.

The evidence-informed standardization of care along disease lines is recommended to improve outcomes and reduce healthcare costs. The aim of this project ...

Medicine

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Cardiac arrhythmias and cardiovascular diseases are a major public health problem in developed countries. A major goal in preventive medicine is the reduction of cardiovascular death by early detection of atrial fibrillation (AF), which may cause stroke, or early detection of life-threatening myocardial ischemia in acute coronary syndrome. Detection of arrhythmia is often challenging if symptoms occur when patients have no chance for immediate electrocardiogram (ECG) diagnostic testing, or if the observation time period is short or an immediate visit to their doctor is not possible. Smartwatches and other wearable devices are able to record a single lead ECG recording, but a single lead ECG is often not sufficient for diagnosis of cardiovascular disorders. Even diagnosis of AF can be difficult with only information from a single lead bipolar ECG. Some smart devices use photoplethysmography for detection of cardiac rhythm, but this technique can only give indirect hints of the underlying cardiac rhythm, is prone to interferences, and cannot be used for detection of myocardial ischemia. A three-lead bipolar ECG like the Einthoven leads used in regular ECGs can add useful information concerning arrhythmia detection or even diagnosis of other cardiovascular diseases like ischemia. Therefore, we describe a protocol for the patient-directed recording of an Einthoven three-lead ECG using a smartwatch.

We describe a protocol for the patient-directed registration of a three lead bipolar electrocardiogram by a smartwatch that functions identically to the Einthoven leads from standard electrocardiograms. This enables patients to record electrocardiograms on their own immediately after the onset of symptoms.

Cardiac arrhythmias and cardiovascular diseases are a major public health problem in developed countries. A major goal in preventive medicine is the ...

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Neurophysiological monitoring is an important goal in the treatment of neurocritical patients, as it may prevent secondary damage and directly impact morbidity and mortality rates. However, there is currently a lack of suitable non-invasive, real-time technologies for continuous monitoring of cerebral physiology at the bedside. Diffuse optical techniques have been proposed as a potential tool for bedside measurements of cerebral blood flow and cerebral oxygenation in case of neurocritical patients. Diffuse optical spectroscopies have been previously explored to monitor patients in several clinical scenarios ranging from neonatal monitoring to cerebrovascular interventions in adults. However, the feasibility of the technique to aid clinicians by providing real-time information at the bedside remains largely unaddressed. Here, we report the translation of a diffuse optical system for continuous real-time monitoring of cerebral blood flow, cerebral oxygenation, and cerebral oxygen metabolism during intensive care. The real-time feature of the instrument could enable treatment strategies based on patient-specific cerebral physiology rather than relying on surrogate metrics, such as arterial blood pressure. By providing real-time information on the cerebral circulation at different time scales with relatively cheap and portable instrumentation, this approach may be especially useful in low-budget hospitals, in remote areas and for&#160;monitoring in open fields (e.g., defense and sports).

Presented here is a protocol for non-invasively monitoring cerebral hemodynamics of neurocritical patients in real-time and at the bedside using diffuse optics. Specifically, the proposed protocol uses a hybrid diffuse optical systems to detect and display real-time information on cerebral oxygenation, cerebral blood flow and cerebral metabolism.

Neurophysiological monitoring is an important goal in the treatment of neurocritical patients, as it may prevent secondary damage and directly impact ...

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

The MACCS (Medical Assistant for Chronic Care Service) platform enables secure sharing of key medical information between patients after kidney transplantation and physicians. Patients provide information such as vital signs, well-being, and medication intake via smartphone apps. The information is transferred directly into a database and electronic health record at the kidney transplant center, which is used for routine patient care and research. Physicians can send an updated medication plan and laboratory data directly to the patient app via this secure platform. Other features of the app are medical messages and video consultations. Consequently, the patient is better-informed, and self-management is facilitated. In addition, the transplant center and the patient's local nephrologist automatically exchange notes, medical reports, laboratory values, and medication data via the platform. A telemedicine team reviews all incoming data on a dashboard and takes action, if necessary. Tools to identify patients at risk for complications are under development. The platform exchanges data via a standardized secure interface (Health Level 7 (HL7), Fast Healthcare Interoperability Resources (FHIR)). The standardized data exchange based on HL7 FHIR guarantees interoperability with other eHealth solutions and allows rapid scalability to other chronic diseases. The underlying data protection concept is in concordance with the latest European General Data Protection Regulation. Enrollment started in February 2020, and 131 kidney transplant recipients are actively participating as of July 2020. Two large German health insurance companies are currently funding the telemedicine services of the project. The deployment for other chronic kidney diseases and solid organ transplant recipients is planned. In conclusion, the platform is designed to enable home monitoring and automatic data exchange, empower patients, reduce hospitalizations, and improve adherence, and outcomes after kidney transplantation.

The MACCS platform&#160;is a comprehensive telemedicine concept aiming at better outcomes after kidney transplantation by sharing key medical information between patients and physicians. A telemedicine team reviews incoming data to detect potential complications and to improve adherence in kidney transplant recipients to achieve better long-term outcomes.

The MACCS (Medical Assistant for Chronic Care Service) platform enables secure sharing of key medical information between patients after kidney ...

The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders

The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle responses in people with neuromuscular disorders, which in turn can improve sensorimotor function when applied appropriately. EPOCS monitors the state of specific target muscles&#8212;e.g., from surface electromyography (EMG) while standing, or from gait cycle measurements while walking on a treadmill&#8212;and automatically triggers calibrated stimulation when pre-defined conditions are met. It provides two forms of feedback that enable a person to learn to modulate the targeted pathway&#39;s excitability. First, it continuously monitors ongoing EMG activity in the target muscle, guiding the person to produce a consistent level of activity suitable for conditioning. Second, it provides immediate feedback of the response size following each stimulation and indicates whether it has reached the target value.
To illustrate its use, this article describes a protocol through which a person can learn to decrease the size of the Hoffmann reflex&#8212;the electrically-elicited analog of the spinal stretch reflex&#8212;in the soleus muscle. Down-conditioning this pathway&#39;s excitability can improve walking in people with spastic gait due to incomplete spinal cord injury. The article demonstrates how to set up the equipment; how to place stimulating and recording electrodes; and how to use the free software to optimize electrode placement, measure the recruitment curve of direct motor and reflex responses, measure the response without operant conditioning, condition the reflex, and analyze the resulting data. It illustrates how the reflex changes over multiple sessions and how walking improves. It also discusses how the system can be applied to other kinds of evoked responses and to other kinds of stimulation, e.g., motor evoked potentials to transcranial magnetic stimulation; how it can address various clinical problems; and how it can support research studies of sensorimotor function in health and disease.

The Evoked Potential Operant Conditioning System EPOCS: A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders

The Evoked Potential Operant Conditioning System aids scientific investigation of sensorimotor function and can administer targeted neurobehavioral training that can impact sensorimotor rehabilitation in neuromuscular disorders. This article describes its capabilities and illustrates its application in modifying a simple spinal reflex to achieve lasting improvement in motor function.

The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle ...

Deep Brain Stimulation for Treatment of Major Depression

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy.

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment 

Deep brain stimulation triggered by a patient-specific neural biomarker of a high-symptom state may better control symptoms of major depressive disorder compared to continuous, open-loop stimulation. This protocol provides a workflow for identifying a patient-specific neural biomarker and controlling the delivery of therapeutic stimulation based on the identified biomarker.

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major ...

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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