Medical alarms have appeared on the Emergency Care Research Institute’s list of top medical hazards four times — twice in the number one spot. According to a recent FDA survey, bad sound design for medical devices accounted for 566 deaths over four years, mostly because the sounds can be so annoying that they get turned down so doctors and nurses can concentrate, leading to potentially deadly consequences.
In this TEDx McMaster talk from February 2021, Michael Schutz, an associate professor of music cognition and percussion, explains how his research with the Music Acoustics Perception Learning (MAPLE) lab is helping to create better alarms — and better patient outcomes.
OHDSI OMOP CDM is one of the most popular clinical data models for health data warehouses. The simple, but clinically motivated data structure is intuitively appealing to clinicians leading to its good adoption. In this respect, it has overtaken HL7-V3 which is more robust but has a steeper learning curve, especially for clinicians. The OHDSI OMOP CDM is widely used in the pharmaceutical industry for drug monitoring.
FHIR is emerging as the defacto standard for health system interoperability, owing largely to its simplicity and the use of existing and popular standards such as REST. As NoSQL databases become more and popular in healthcare, FHIR can also be a good persistence schema. It aligns well with search technologies such as elasticsearch.
As both standards are popular, conversion from one to the other may be commonly required. Researchers at Georgia Tech have an open-source tool – GT-FHIR2 – for mapping an existing OHDSI OMOP CDM database as FHIR endpoint. However, conversion between existing systems may not be easy with a full-stack solution.
I have a simpler solution that I believe will be useful in the following scenarios:
To export a cohort to a FHIR based analytics tool.
To load new resources to OMOP CDM databases for incremental ETL.
Omopfhirmap is a command-line tool for mapping a OHDSI cohort, defined in ATLAS, to a FHIR bundle that can be optionally submitted to a FHIR server for processing. Conversely, it can process a FHIR bundle and add resources to an existing CDM database ignoring duplicates. Unlike GT-FHIR2, the OMOP on FHIR Project at Georgia Tech omopfhirmap does not expose OMOP database as FHIR endpoints.
I have used spring-boot and JPA for easy wiring of services and abstraction of database and the hapi-fhir as it is an obvious choice for any java based FHIR applications. It is still work in progress and any help will be appreciated (Refer to CONTRBUTING.md).
The COVID-19 pandemic brought to light many of the vulnerabilities in our data collection and analytics workflows. Lack of uniform data models limits the analytical capabilities of public health organizations and many of them have to re-invent the wheel even for basic analysis. As many other sectors embrace big data and machine learning, many healthcare analysts are still stuck with the basic data wrenching with Excel.
The OHDSI OMOP CDM (Common data model) for observational data is a popular initiative for bringing data into a common format that allows for collaborative research, large-scale analytics, and sharing of sophisticated tools and methodologies. Though OHDSI OMOP CDM is primarily for patient-centred observational analysis, mostly for clinical research, it can be used with minor tweaks for public health and epidemiologic data as well. We have written about some of the technical details here.
The OHDSI OMOP CDM is relatively simple and intuitive for clinical teams than emerging standards such as FHIR. Though the relational database approach and some of the software tools associated with OHDSI OMOP CDM are archaic, the data model is clinically motivated. There is an ecosystem of software tools for many of the analytics tools that can be used out of the box. The Observational Medical Outcomes Partnership (OMOP) CDM, now in its version 6.0, has simple but powerful vocabulary management. OHDSI OMOP CDM is a good choice for healthcare organizations moving towards health data warehousing and OLAP.
One weakness of OHDSI is the lack of tools for efficient ETL from existing EHR and HIS. Converting existing EHR data to the CDM is still a complex task that requires technical expertise. During the additional “home time” during the COVID pandemic, I have created three software libraries for ETL tool developers. These libraries in Python, .NET and Golang encapsulated the V6.0 CDM and helps in writing and reading data from a variety of databases with the V6.0 tables. The libraries also support creating the CDM tables for new databases and loading the vocabulary files.
These libraries might save you some time if you are building scripts for ETL to CDM. They are all open-source and free to use in your tools. Do give me a shout if you find these libraries useful and please star the repositories on GitHub.
Discharge Abstract Database (DAD) is a Canada-wide database of hospital admission and discharge data excluding the province of Quebec, maintained by the Canadian Institute for Health Information (CIHI). The data points in DAD include patient demographics, comorbidities coded in the International Statistical Classification of Diseases and Related Health Problems (ICD), interventions encoded in the Canadian Classification of Health Interventions (CCI) and the length of stay. DAD is the de-identified 10% sample available under the Data Liberation Initiative (DLI) for academic researchers. DAD is arguably the most comprehensive country-wide discharge dataset in the world.
Discharge Abstract Database is used for creating public reports for hospitals, researchers, and the general public. DAD data has also been used for disease-specific research and analysis, including public health, disease surveillance, and health services research. CIHI provides DAD in the SPSS (.sav) format with each record having horizontal fields for 20 comorbidities and 25 interventions. The format is not ideal for slicing and dicing the data for visualization for clinicians to obtain clinical insights.
DADpy provides a set of functions for using the DAD dataset for machine learning and visualization. The package does not include the dataset. Academic researchers can request the DAD dataset from CIHI. This is an unofficial repo, and I’m not affiliated with CIHI. Please retain the disclaimer below in forks.
from dadpy import DadLoad
from dadpy import DadRead
# with the trailing slash
dl = DadLoad('/path/to/dad/sample/spss/sav/file/') # clin_sample_spss.sav
dr = dad_read(dl.sample)
# records with obesity as pandas df
# Partial gastrectomy for repair of gastric diverticulum
# comorbidities as dict for visualization
print(dr.comorbidity('E66')) # Obesity
# co-occurance of treatments as dict
print(dr.interventions('1NF80')) # Partial gastrectomy for repair of gastric diverticulum
# Get the one-hot-encoded vector for machine learning
dr.vector(dr.has_diagnosis('E66'), significant_chars=3, include_treatments=True)
We use poetry for development. PR are welcome. Please see CONTRIBUTING.md in the repo. Start by renaming .env.example to .env and add path for tests to run. Add jupiter notebooks to the notebook folder. Include the disclaimer below.
Disclaimer: Parts of this material are based on the Canadian Institute for Health Information Discharge Abstract Database Research Analytic Files (sampled from fiscal years 2016-17). However the analysis, conclusions, opinions and statements expressed herein are those of the author(s) and not those of the Canadian Institute for Health Information.
Let us know if you use DADpy for creating interesting jupyter notebooks.
This is a simple application to convert a CSV file to a FHIR bundle and post it to a FHIR server in Golang. The OSCAR EMR has an EForm export tool that exports EForms to a CSV file that can be downloaded. This tool can load that CSV file to a FHIR server for consolidated analysis. This tool can be used with any CSV, if columns specified below (CSV format section) are present.
This is useful for family practice groups with multiple OSCAR EMR instances. Analysts at each site can use this to send data to a central FHIR server for centralized data analysis and reporting. Public health agencies using OSCAR or similar health information systems can use this to consolidate data collection.
How to build
First go get all dependencies This package includes three tools (Go build them separately from the cmd folder):
Fhirpost: The application for posting the csv fie to the FHIR server
Serverfhir: A simple FHIR server for testing (requires mongodb). We recommend using PHIS-DW for production.
Report: A simple application for descriptive statistics on the csv file
Format of the CSV file
Using vocabulary such as SNOMED for field names in the E-Form is very useful for consolidated analysis.
Each record should have:
demographicNo → The patient ID dateCreated efmfid → The ID of the eform fdid → The ID of the each form field. (The Eform export csv of OSCAR typically has all these fields and requires no further processing)
Bundle with unique patients. All columns mapped to observations.
Submitter mapped to Practitioner.
Document type bundle with composition as the first entry
Unique fullUrls are generated.
PatientID is location + demographicNo
Budle of 1 composition, 1 practitioner, 1 or more patients, and many observations
Validates with R4 schema
How to use:
Change the settings in .env
You can compile this for Windows, Mac or Linux. Check the fhirmap.go file and make any desired changes. You should be able to figure out the mapping rules from this file.
It reads data.csv file from the same folder by default. (can be specified by the -file commandline argument: fhirpost -file=data.csv)
Start mongodb and run server and fhirpost in separate windows for testing.
On windows, you can just double-click executables to run. (Closes automatically after run)
Privacy and security: This application does not encrypt the data. Use it only in a secure network.
Disclaimer: This is an experimental application. Use it at your own risk.
Natural Language Processing (NLP) on the vast amount of data captured by electronic medical records (EMR) is gaining popularity. The recent advances in machine learning (ML) algorithms and the democratization of high-performance computing (HPC) have reduced the technical challenges in NLP. However, the real challenge is not the technology or the infrastructure, but the lack of interoperability — in this case, the inconsistent use of terminology systems.
NLP tasks start with recognizing medical terms in the corpus of text and converting it into a standard terminology space such as SNOMED and ICD. This requires a terminology mapping service that can do this mapping in an easy and consistent manner. The Unified Medical Language System (UMLS) terminology server is the most popular for integrating and distributing key terminology, classification and coding standards. The consistent use of UMLS resources leads to effective and interoperable biomedical information systems and services, including EMRs.
The umlsjs package is available on GitHub and the NPM. It is still work in progress and any coding/documentation contributions are welcome. Please read the CONTRIBUTING.md file on the repository for instructions. If you use it and find any issues, please report it on GitHub.
COVID-19 Open Research Dataset (CORD-19) is a dataset of approximately 47,000 scholarly articles, about COVID-19, SARS-CoV-2, and related coronaviruses made free to the research community by a coalition of research groups. The articles are provided as JSON files for the global research community to apply natural language processing.
Elasticsearch (ES) is a Lucene based text search engine using schema-free JSON documents. Elasticsearch is fast and has clients libraries available for most programming languages including python. Loading the COVID-19 data on to an ES instance will be helpful for easy search and analysis, all within the comfort of the jupyter notebook. The availability of the Apache spark (spark) connector makes the exchange of data between ES and spark easy. I have listed below, the simple steps to load the files to an ES instance.
+——————–+——————–+——————–+——————–+——————–+ | paper_id| metadata.title| metadata.authors| abstract.text| body_text.text| +——————–+——————–+——————–+——————–+——————–+ |28b10724357672324…| | | |[i c , a n t i f …| |1aa3e788fc6b03c14…|Dark Proteome of …|[[[Himachal Prade…|[Recently emerged…|[World health org…| |558d318e1655da9f5…|Connectivity anal…|[[[University of …|[We utilized a ce…|[Schizophrenia is…| +——————–+——————–+——————–+——————–+——————–+ only showing top 3 rows
STEP 4: Create an index and write the spark df into ES:
from elasticsearch import Elasticsearch
es = Elasticsearch()
STEP 5: Do the search!
es.search(index="covid", q="metadata.title:(CD14 OR CD8)")
That’s it! You can now use it for search and do analytics on the returned records. Next, I will show you how to use QRMine on CORD-19!
McMaster University researchers have developed a tool to share with the international health sciences community which can help determine how the coronavirus that causes COVID-19 is spreading and whether it is evolving.
Simply put, the tool is a set of molecular ‘fishing hooks’ to isolate the virus, SARS-CoV-2, from biological samples. This allows laboratory researchers to gain insight into the properties of the isolated virus COVID-19 by then using a technology called next-generation sequencing.
“You wouldn’t use this technology to diagnose the patient, but you could use it to track how the virus evolves over time, how it transmits between people, how well it survives outside the body, and to find answers to other questions,” said principal investigator Andrew McArthur, associate professor of biochemistry and biomedical sciences, and a member of the Michael G. DeGroote Institute for Infectious Disease Research (IIDR) at McMaster.
“Our tool, partnered with next-generation sequencing, can help scientists understand, for example, if the virus has evolved between patient A and patient B.”
McArthur points out that the standard technique to isolate the virus involves culturing it in cells in contained labs by trained specialists. The McMaster tool gives a faster, safer, easier and less-expensive alternative, he said.
“Not every municipality or country will have specialized labs and researchers, not to mention that culturing a virus is dangerous,” he said.
“This tool removes some of these barriers and allows for more widespread testing and analyses.”
First author Jalees Nasir, a PhD candidate in biochemistry and biomedical sciences at McMaster, has been working with McMaster and Sunnybrook Health Sciences Centre researchers to develop a bait capture tool that can specifically isolate respiratory viruses. When news recently broke of COVID-19, Nasir knew he could develop a “sequence recipe” to help researchers to isolate the novel virus more easily.
“When you have samples from a patient, for example, it can consist of a combination of virus, bacteria and human material, but you’re really only interested in the virus,” Nasir said. “It’s almost like a fishing expedition. We are designing baits that we can throw into the sample as hooks and pull out the virus from that mixture.”
The decision was made to release the sequences publicly without the normal practice of peer-review or clinical evaluation to ensure this tool was available to all quickly, recognizing the urgency of the situation, said McArthur.
The research team plans to collaborate with Sunnybrook for further testing but also hopes other scientists can quickly perform their own validation.
McArthur added that a postdoctoral fellow in his lab, David Speicher, is currently communicating details of the technology to the international clinical epidemiology community.
“Since we’re dealing with an outbreak, there was no value in us doing a traditional academic study and the experiments,” said McArthur. “We designed this tool and are releasing it for use by others.
“In part, we’re relying on our track record of knowing what we are doing, but we’re also relying on people who have the virus samples in hand being able to do the validation experiment so that it’s reliable.”
The research was funded by the Comprehensive Antibiotic Resistance Database at McMaster.
An international research collaboration has discovered a new bacteria-killing toxin that shows promise of impacting superbug infectious diseases.
The discovery of this growth-inhibiting toxin, which bacteria inject into rival bacteria to gain a competitive advantage, was published today in the journal Nature.
The discovery is the result of teamwork by co-senior authors John Whitney, assistant professor of the Department of Biochemistry and Biomedical Sciences at McMaster University, and Mike Laub, professor of biology at the Massachusetts Institute of Technology (MIT).
Whitney and his PhD student Shehryar Ahmad at McMaster’s Michael G. DeGroote Institute for Infectious Disease Research were studying how bacteria secrete antibacterial molecules when they came across a new toxin. This toxin was an antibacterial enzyme, one the researchers had never seen before.
After determining the molecular structure of this toxin, Whitney and Ahmad realized that it resembles enzymes that synthesize a well-known bacterial signalling molecule called (p)ppGpp. This molecule normally helps bacteria survive under stressful conditions, such as exposure to antibiotics.
“The 3D structure of this toxin was at first puzzling because no known toxins look like enzymes that make (p)ppGpp, and (p)ppGpp itself is not a toxin,” said Ahmad.
Suspecting the toxin might kill bacteria by overproducing harmful quantities of (p)ppGpp, the McMaster team shared their findings with Laub, an investigator of the U.S. Howard Hughes Medical Institute.
Boyuan Wang, a postdoctoral researcher in the Laub lab who specializes in (p)ppGpp signaling, examined the activity of the newly discovered enzyme. He soon realized that rather than making (p)ppGpp, this enzyme instead produced a poorly understood but related molecule called (p)ppApp. Somehow, the production of (p)ppApp was harmful to bacteria.
The researchers determined that the rapid production of (p)ppApp by this enzyme toxin depletes cells of a molecule called ATP. ATP is often referred to as the ‘energy currency of the cell’ so when the supply of ATP is exhausted, essential cellular processes are compromised and the bacteria die.
“I find it absolutely fascinating that evolution has essentially “repurposed” an enzyme that normally helps bacteria survive antibiotic treatment and, instead, has deployed it for use as an antibacterial weapon,” said Whitney.
The research conducted at McMaster University was funded by the Canadian Institutes for Health Research and is affiliated with the CIHR Institute for Infection and Immunity (CIHR-III) hosted at McMaster University with additional funding from the David Braley Centre for Antibiotic Discovery. The research at MIT was supported by the Howard Hughes Medical Institute and the U.S. National Institutes of Health.
“This is an important discovery with potential implications for developing alternatives to antibiotics, a global priority in the fight against antimicrobial resistance. It is heartening to see that young Canadian researchers like Dr. Whitney are thriving and emerging as leaders in this area,” said Charu Kaushic, scientific director of the CIHR-III and a professor of pathology and molecular medicine at McMaster.
OSCAR is a spring java application deployed in a tomcat container with MySQL database backend. OSCAR project being relatively old, with few users outside Canada, has struggled to keep pace with the developments in the electronic health records domain. However, OSCAR is still useful and popular among family physicians and some public health organizations as it is free and well supported.
Oscar is known for its support for the billing workflow, data collection forms (eForms) and comprehensive patient charts (eCharts). Some of the limitations of OSCAR include lack of scalability beyond a handful of users and limited support for data analytics. Oscar by design is hard to be virtualized as a docker container. Availability of a docker container is crucial for sustainable and fault-tolerant deployment on the cloud and distributed systems such as Kubernetes.
Docker is the world’s leading software container platform, used mostly for DevOps. Docker is also useful for developers to set up a development environment in a few easy steps. I was one of the first few who worked on virtualizing OSCAR. Thanks for all those who forked (and hopefully used) this repository.
I have continued my work on OSCAR docker container and has been successful in creating a (reasonably stable) container. It is now available on docker hub. I am now working on a fault-tolerant deployment of OSCAR in customized hardware. I (and some of my friends who know about and encouraged this project) call it OSCAR in a BOX! It has multiple instances of OSCAR with each instance capable of self-healing when a JAVA process hangs (fairly common for OSCAR). The database is replicated, and both the database and documents incrementally back up to an additional disk.
OSCAR in a BOX is ideal for family physicians who wish to adopt OSCAR but does not have the technical support for maintaining the system. OSCAR in a BOX is plug and play and is virtually maintenance-free. The virtualization workflow will also be useful for existing bigger user groups reeling under the sluggish pace of OSCAR. Please let me know if anybody is interested in collaborating.