QL4POMR is a new vision to transform healthcare through interlinking everything from the bedside along with discoveries and knowledge available at the bench. It is kind of Translational Informatics that uses the notion of graph to bridge both sides based on sound standards. QL4POMR brings several emerging methods to establish this bridge for clinical problem representation, clinical data integration, improving diagnosis, staging, prognosis, and treatment of diseases. QL4POMR integrates biomedical multi-omics data knowledge with the clinical practice data to improve the power of predicting clinical outcomes. Since the electronic health records (EHR) are commonly used to store and analyze patient data, and then it seems straight-forward to perform research on modeling EHR data as graphs and bridge the bedside with the bench based on interlinked graphs. QL4POMR vision at the bedside starts with charting clinical cases according to SOAP note and building the clinical case graph based on common clinical problem list. This interlinked vision was originally introduced by Lawrence Weed (MD) in his Problem-Oriented Medical Record (POMR) vision to solve interoperability in healthcare but was not implemented as the technologies of EHRs largely uses a tabular/relational format that is too different from the graph-based vision. QL4POMR based on the SOAP Schema can translate and link clinical data to any EHR system or bench data repository server based on the GraphQL API. Based on this flexible API QL4POMR managed to link clinical cases to the standard HL7 FHIR electronic Healthcare Record as well as to produce the HL7 IPS (International Patient Summary). Based on the same API, QL4POMR interlinked patient cases and their records to the knowledge available at the bench. QL4POMR managed to connect to important biomedical repositories like the OpenTargets and DrugBank. Based on this graph-based vision, clinician at the bedside can see clearly alternative medications to what they have prescribed, they can see if their prescribed drugs can have some adverse interactions or if a patient is a case of a polypharmacy that requires deprescribing some of these medications. QL4POMER also help the bench side by providing an extended clinical trial verifications from the bedside. There are many other question that can be answered based on this graph-based translational vision to include the genetic data and the chemical components of the prescribed drugs. It is important to mention that QL4POMR is a result of my individual research as supported by recent NSERC (2020-2023) and large collaboration with Dr. Fiaidhi and the Lakehead Graduate Students team as well as with Thunder Bay Regional Health Science Center (TBRHSC) through our 5 Years MITACS project (2021-2025).
Collaborative Research with Thunder Bay Regional Health Science Center
The MITACS Research with TBRHSC is a five year project 2021-2025. It is a large Research project that started in May 1st, 2021 to develop the next generation Graph-Based Problem Oriented Medical record in collaboration with Thunder Bay Regional Health Science Center. I share this research with Dr. Jinan Fiaidhi as principle investigator and Dr. Arnold Kim (MD) as our TBRHSC collaborator. This research has been also extended to cover remote patient monitoring and consultation through my new NSERC Discovery Grant. Both research uses my graph-based translational methods.
QL4POMR Research Objectives
There are so many objectives behind my cutting edge research. The first and most important objective is to bridge the gap between the bed-side clinical practice and the bench-side biomedical research where graph-based approaches can be used for the translational knowledge needed for building this bridge.
The second objective is the use of a standard graph-based schema for representing clinical cases at the bedside. QL4POMR uses the SOAP note schema (Subjective as encountered by the patient, Objectives as conducted by the examining physician observations, Assessments from the requested lab and diagnostic imaging tests and the Plan of treatment). The SOAP note was invented by Dr. Lary Weed (MD) and widely used in clinical education and practice. The third objective is to align the SOAP attributes to their corresponding medical problems and build a Problem-Oriented Medical Record (POMR) system that is centered on the notion of of the problem set. QL4POMR translates the SOAP/POMR schema into the standard HL7 FHIR (Resources) schema. The fourth objective is relate the POMR clinical description (via the FHIR Resources Schema) to the corresponding biomedical knowledge available at the bench including data about drugs, diseases, genetics, adverse events, etc. through utilizing notable open source repositories like the OpenTargets and DrugBank. The fifth objective is to employ the constructed bridge between the bedside and the bench side to provide focused analytics and to reveal important clinical patterns and links to benefit clinicians at the bedside (e.g. solving the ADE effects of polypharmacy) and the biomedical/pharma scientists with a real world clinical monitoring (e.g. clinical trials from the bedside).
To achieve all these objectives, this research project have constructed a prototype (QL4POMR) for the new problem oriented medical record (POMR) using the emerging GraphQL API to deal with semi structured care design and model the lower ends of SOAP as a graph. The early results of our research have demonstrated an amazing success to translate the SOAP cases into the standard HL7 FHIR records based on the HL7 FHIR server. The new QL4POMR system uses a CRUD (Create, Read, Update, Delete) interface to fetch and save semi-structured care data on the actual FHIR system. We are adding more components to our QL4POMR to link it to other biomedical data through building a Gatsby Data Layer that can incorporate schema of external data sources without having to have these data sources inside QL4POMR. The use of Gatsby API through the GraphQL server prove very effective in linking to external biomedical sources such as OpenTargets and DrugBank. Our efforts is continuing and we are delighted that we have passionate group including our graduate students (e.g. Darien Sawyer, Mehdi Lamouchi and Peter Sertic).
The overall QL4POMR Design:
Watch our Webinar on QL4POMR
Recent QL4POMR Publications:
(1) Establishment of a mindmap for medical e-Diagnosis as a service for graph-based learning and analytics
S Mohammed, J Fiaidhi
Springer Neural Computing & Applications 33 (18), 1-12, 2021
(2) QL4POMR Interface as a Graph-Based Clinical Diagnosis Web Service
Sabah Mohammed, Jinan Fiaidhi, Darien Sawyer
IEEE 11th International Conference on Logistics, Informatics and Service Sciences (LISS2021)
(3) Empowering GraphQL Based Problem Oriented Medical Record Systems using a Data Layer
Sabah Mohammed, Jinan Fiaidhi
International Journal of Future Generation Communication and Networking (IJFGCN), Volume 14, Issue 3, Pages 1-12,
(4) Generating Physician Standing Orders for Unplanned Care Scenarios using the HL7 FHIR Patient Summaries,
Sabah Mohammed, Jinan Fiaidhi and Darien Sawyer, The 9th IEEE International Conference on E-Health and Bioengineering - EHB 2021 Grigore T. Popa University of Medicine and Pharmacy, Web Conference, Romania, November 18-19, 2021.
(5) Problem Oriented Diagnostic Service for Describing Clinical Cases based on the GraphQL POMR Approach,
Sabah Mohammed, Jinan Fiaidhi and Darien Sawyer, Dec. 9-12, IEEE BIBM 2021
(6) GraphQL Patient Case Presentation using the Problem Oriented Medical Record Schema,
Sabah Mohammed, Jinan Fiaidhi and Darien Sawyer, IEEE Big Data, December 15-18, 2021
(7) Introducing QL4POMR CRUD BFF for Processing IPS Standard Patient Summary Report on FHIR
Sabah Mohammed; Jinan Fiaidhi; Darien Sawyer
4th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2022 (IEEE ECBIOS 2022)
(8) Value-Based Healthcare Translational Data Analytics using the Problem Oriented Medical Record Graph Representation
Sabah Mohammed; Jinan Fiaidhi; Darien Sawyer
Rochester, Minnesota, United States
11-14 June 2022
ICHI 2022 Conference Link: https://ohnlp.github.io/IEEEICHI2022/