The document proposes an interoperability reference architecture (HISO 10040) for New Zealand's health sector to enable consistent and interconnected health information exchange. It outlines principles such as aligning with national strategy, investing in standardized clinical information models, and adopting proven international standards. The reference architecture uses a common clinical content model expressed through archetypes to define clinical concepts in a consistent way. This will allow health information to be exchanged as structured messages between different health IT systems using a service-oriented approach. The goals are to support integrated and shared care through consistent interoperability across the health sector.
CER HUB An Informatics Platform for Conducting Compartive Effectiveness with ...HMO Research Network
The document describes the CER Hub, an informatics platform for conducting comparative effectiveness research using electronic medical record data. The CER Hub allows researchers to develop standardized processors to generate research datasets from heterogeneous EMR systems. It facilitates collaborative projects to address questions like evaluating asthma control and smoking cessation treatments. Initial projects through the CER Hub involve developing measures of asthma control and comparing the effectiveness of treatment intensification options using EMR data from six health systems.
This document announces an event called Clinician's Challenge 2011 that is seeking submissions from clinicians about issues with health IT. It encourages clinicians to submit entries for great prizes, with winning cases posed to vendors to develop solutions. The event will take place November 7-9 in Rotorua, New Zealand and invites interaction from attendees.
What if we never agree on a common health information model?Koray Atalag
In this talk I will touch on some hard problems in health informatics around working with structured data and why we can’t link and reuse them with ease. The essence of the problem is that, while clinicians can perfectly understand each other, IT systems can’t. Traditional IT requires formally defined common terminology, meta-data, data and process definitions. While Medicine is mostly accepted as positive science, yet the great variation in the body of knowledge and practice is often seen as ‘Art’. Ignoring this bit, IT people tend to develop all-inclusive common information models (almost always too complex to implement) and expect everybody adhere to that. Clinicians love to do things a bit differently and of course don’t buy into that! Maybe they are right! Maybe we don’t have to agree on a uniform model at all. This is the basic assumption of the openEHR methodology which I will describe by giving clinical examples. The main premise of this approach is to effectively separate tasks of healthcare and technical professionals. Clinicians can easily define their information needs as they like using visual tools – called Archetypes which are essentially maximal data sets. These computable artefacts, built using a well defined set of technical building blocks, are then fed into the technical environment to integrate data or develop software. Lastly the free web based openEHR Clinical Knowledge Manager portal provides collaborative Archetype development and ensures semantic consistency among different models.
I gave this prezo to Auckland Regional Clinical IS Leadership Group on Feb 21, 2014. It shows how difficult it can be to deal with certain kinds of health information when developing systems by an impressive example (originally from Dr. Sam Heard). Therefore we need rigorous and scientific methods to tackle this - in this case using openEHR's multi-level modelling approach to create a single content model from which all health information exchange payload definitions will be derived. New Zealand's Interoperability Reference Architecture (HISO 10040) is underpinned by openEHR Archetypes to create this content model. The bottom line of the prezo is that almost every national programme starts health information standardisation from the wrong place; most of them are complex technical speficifications, like CDA, which are almost impossible for clinicians to comprehend and provide feedback. The process is flawed! Instead it should start from simple to understand representations, such as simple diagrams, mindmaps etc.and then handed over to techies once clinical validity and utility is agreed upon.That's the beauty of Archetype approach - great tooling and the Clinical Knowledge Manager (CKM) enable clinicians and other domain experts to collaborate and develop clinical models easily.
CER HUB An Informatics Platform for Conducting Compartive Effectiveness with ...HMO Research Network
The document describes the CER Hub, an informatics platform for conducting comparative effectiveness research using electronic medical record data. The CER Hub allows researchers to develop standardized processors to generate research datasets from heterogeneous EMR systems. It facilitates collaborative projects to address questions like evaluating asthma control and smoking cessation treatments. Initial projects through the CER Hub involve developing measures of asthma control and comparing the effectiveness of treatment intensification options using EMR data from six health systems.
This document announces an event called Clinician's Challenge 2011 that is seeking submissions from clinicians about issues with health IT. It encourages clinicians to submit entries for great prizes, with winning cases posed to vendors to develop solutions. The event will take place November 7-9 in Rotorua, New Zealand and invites interaction from attendees.
What if we never agree on a common health information model?Koray Atalag
In this talk I will touch on some hard problems in health informatics around working with structured data and why we can’t link and reuse them with ease. The essence of the problem is that, while clinicians can perfectly understand each other, IT systems can’t. Traditional IT requires formally defined common terminology, meta-data, data and process definitions. While Medicine is mostly accepted as positive science, yet the great variation in the body of knowledge and practice is often seen as ‘Art’. Ignoring this bit, IT people tend to develop all-inclusive common information models (almost always too complex to implement) and expect everybody adhere to that. Clinicians love to do things a bit differently and of course don’t buy into that! Maybe they are right! Maybe we don’t have to agree on a uniform model at all. This is the basic assumption of the openEHR methodology which I will describe by giving clinical examples. The main premise of this approach is to effectively separate tasks of healthcare and technical professionals. Clinicians can easily define their information needs as they like using visual tools – called Archetypes which are essentially maximal data sets. These computable artefacts, built using a well defined set of technical building blocks, are then fed into the technical environment to integrate data or develop software. Lastly the free web based openEHR Clinical Knowledge Manager portal provides collaborative Archetype development and ensures semantic consistency among different models.
I gave this prezo to Auckland Regional Clinical IS Leadership Group on Feb 21, 2014. It shows how difficult it can be to deal with certain kinds of health information when developing systems by an impressive example (originally from Dr. Sam Heard). Therefore we need rigorous and scientific methods to tackle this - in this case using openEHR's multi-level modelling approach to create a single content model from which all health information exchange payload definitions will be derived. New Zealand's Interoperability Reference Architecture (HISO 10040) is underpinned by openEHR Archetypes to create this content model. The bottom line of the prezo is that almost every national programme starts health information standardisation from the wrong place; most of them are complex technical speficifications, like CDA, which are almost impossible for clinicians to comprehend and provide feedback. The process is flawed! Instead it should start from simple to understand representations, such as simple diagrams, mindmaps etc.and then handed over to techies once clinical validity and utility is agreed upon.That's the beauty of Archetype approach - great tooling and the Clinical Knowledge Manager (CKM) enable clinicians and other domain experts to collaborate and develop clinical models easily.
Beating Bugs with Big Data: Harnessing HPC to Realize the Potential of Genomi...Tom Connor
Introducing the HPC challenges associated with developing a set of clinical microbial genomics services in the NHS in Wales. Demonstrating the potential of these technologies, and the impact it is already having for the patients of the Welsh NHS.
Content Modelling for VIEW Datasets Using ArchetypesKoray Atalag
This one also I presented at the HINZ conference.
ABSTRACT:
Use of health information for multiple purposes maximises its value. A good example is PREDICT, a clinical decision support system which has been used in New Zealand for a decade. Collected data are linked and enriched with a number of databases, including national collections, laboratory tests and pharmacy dispensing. We are proposing a new model-driven approach for data management based on openEHR Archetypes for the purpose of improving data linkage and future-proofing of data. The study looks at feasibility of building a content model for PREDICT - a methodology underpinning the Interoperability Reference Architecture. The main premise of the content model will be to provide a canonical model of health information which will be used to align incoming data from other data sources. With this approach it is possible to extend datasets without breaking semantics over long periods of time – a valuable capability for research. The content model was developed using existing archetypes from openEHR and NEHTA repositories. Except for two checklist type items, reused archetypes can faithfully represent the whole PREDICT dataset. The study also revealed we will need New Zealand specific extensions for demographic data. Use of archetype based content modelling can improve secondary use of clinical data.
The document discusses a project to analyze and predict sepsis early using clinical data. It aims to predict sepsis 6 hours before clinical diagnosis to allow for earlier treatment. The author handles missing data and class imbalance in a large dataset. Features are engineered and selected. Decision trees and XGBoost models are used for prediction, achieving partial success. Further research is needed on time-series modeling, feature importance, and model performance with a domain expert.
Implementation and Use of ISO EN 13606 and openEHRKoray Atalag
This was the prezo for the EMBC 2013 tutorial in Osaka, Japan. Intended for an introduction to the standards and technicalities and implementation of openEHR - which is the original formalism.
Big Data Analytics for Treatment Pathways John CaiJohn Cai
This document discusses using real-world big data analytics to understand treatment pathways. It begins by explaining the need for real-world evidence from real-world data to assess effectiveness and outcomes beyond randomized clinical trials. It then describes the volume, variety, and velocity characteristics of real-world big data from sources like claims, EMRs, surveys, and devices. Technical challenges of reconstructing complex patient journeys are discussed. Hadoop and MapReduce are presented as a potential solution by breaking the work into mappers that extract patient data and reducers that organize it into timelines. Examples are given of how this could enable cost, pathway, and outcomes analyses to better inform decision making.
TransCelerate is a nonprofit organization that aims to accelerate medical research by improving collaboration across the pharmaceutical industry. It has developed a Common Protocol Template (CPT) to standardize clinical trial protocols. The CPT provides a streamlined template for protocol content and format to make protocols easier to interpret, reduce complexity and costs, and enable automation. The CPT benefits various stakeholders by improving efficiency and quality. Its adoption by sponsors is valuable as it leverages industry expertise, supports compliance, and balances quality improvements with efficiency gains over time.
Impact Of a Clinical Decision Support Tool on Asthma Patients with Current As...Yiscah Bracha
The document summarizes research on the effect of computerized decision support (CDS) on the percentage of asthma patients with asthma action plans. The research found:
1) Implementation of a CDS tool at clinics led to increases in the percentage of pediatric patients with current asthma action plans, especially at clinics that previously lacked paper templates.
2) For adults, clinics that emphasized asthma action plans and had physicians start using the CDS tool saw increases, while clinics without paper templates saw physicians begin using the tool.
3) Statistical analysis showed the CDS tool had an initial positive effect at one pediatric clinic that oscillated over time, while having no significant effect at other clinics, possibly due to pre-existing tendencies of physicians to
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
openEHR Approach to Detailed Clinical Models (DCM) Development - Lessons Lear...Koray Atalag
Presented at Health Informatics New Zealand (HINZ 2017) Conference, 1-3 Nov 2017, Rotorua, New Zealand. Based on my Masters student Peter Wei's research. Authorship: Ping-Cheng Wei, Koray Atalag and Karen Day from the University of Auckland.
Health research, clinical registries, electronic health records – how do they...Koray Atalag
This is a talk I gave at my own organisation - National Institute for Health Innovation (NIHI) of the University of Auckland on 6 Aug 2014. Abstract as follows:
In this talk I’ll first cover the topic of clinical registry – an invaluable tool for supporting clinical practice but also gaining momentum in research and quality improvement. NIHI has been very active in this space: we have delivered the prestigious and highly successful National Cardiac Registry (ANZACS-QI) together with VIEW research team and also very recently launched the Gestational Diabetes Registry with Counties Manukau DHB & Diabetes Projects Trust. A few others are in likely to come down the line. This is a huge opportunity for health data driven research and NIHI to position itself as ‘the health data steward’ in the country given our independent status and existing IT infrastructure and “good culture” of working with health data . NIHI’s ‘health informatics’ twist in delivering these projects is how we go about defining ‘information’ – using a scientifically credible and robust methodology: openEHR. This is an international (and now national too) standard to non-ambiguously define health information so that they are easy to understand and also are computable. We build software (even automatically in some cases!) using models created by this formalism. I’ll give basics of openEHR approach and then walk you through how to make sense out of all these. Hopefully you may have an idea about its ‘value proposition’ (as business people call) or Science merit as I like to call it ;)
Michele Tonutti - Scaling is caring - Codemotion Amsterdam 2019Codemotion
A key challenge we face at Pacmed is quickly calibrating and deploying our tools for clinical decision support in different hospitals, where data formats may vary greatly. Using Intensive Care Units as a case study, I’ll delve into our scalable Python pipeline, which leverages Pandas’ split-apply-combine approach to perform complex feature engineering and automatic quality checks on large time-varying data, e.g. vital signs. I’ll show how we use the resulting flexible and interpretable dataframes to quickly (re)train our models to predict mortality, discharge, and medical complications.
This document summarizes a presentation about identifying deficiencies in long-term condition management using electronic medical records. It discusses using data mining of electronic medical records to analyze hypertension management and electronic referrals. Case studies show opportunities for improved monitoring and treatment of long-term conditions were identified. The presentation encourages using available electronic health record data to help improve healthcare processes and outcomes.
Ms. Drury outlines the EHR world for these Davies Winners before ARRA and the EHR Incentive Program existed, sharing the environment and the motivation for these privately owned physician practices who have been recognized by HIMSS as Davies Ambulatory Award Winners. The HIMSS Nicholas E. Davies Award of Excellence recognizes excellence in the implementation and use of health information technology, specifically electronic health records (EHRs), for healthcare organizations, independent physician practices and public health systems. The HIMSS process of evaluating applications from these practices and validating the use and value of HIT is rigorous for the applicants and for the HIMSS Ambulatory Award Committee.
The document discusses an open-source electronic health record (EHR) system called Oscar and describes its architecture and features. It provides examples of how Oscar has been used in radiotherapy settings and primary care clinics. The document also discusses a personal health record (PHR) module called MyOSCAR that is integrated with Oscar. MyOSCAR allows patients to access and share their health records. Two pilot studies are summarized that examine the use of MyOSCAR for blood pressure management and collecting drug safety data from patients. The studies found high completion rates of tasks in MyOSCAR and positive feedback from patients wishing to continue using the application.
"How do Professional Record Standards Support Timely Communication & Information Flows for all Participants in Health & Social Care"? Gurminder khamba (Clinical Lead for Secondary Care, HSCIC) discusses this question at the Healthcare Efficiency Through Technology Expo 2013.
The Diabetes Discovery Project at Austin Health aimed to use their Cerner EMR system to routinely test HbA1c levels on inpatients over 54 to identify undiagnosed and poorly controlled diabetes. Testing of over 5,000 patients found 5% had undiagnosed diabetes and 29% had known diabetes. Higher HbA1c levels were associated with increased hospital admissions and longer lengths of stay for surgical patients. The project demonstrated using health IT to identify diabetes management opportunities. Ongoing work includes refining protocols and expanding to other patient populations.
This document summarizes a presentation on using data and informatics to improve allied health services. It discusses the history of allied health and challenges with data collection. Examples are provided of projects in New Zealand that used data to enhance patient and clinician experiences, reduce hospital-acquired infections, and inform staffing needs. The presentation emphasizes standardizing data to facilitate benchmarking and applying knowledge gained from data analysis to drive improvements in allied health.
More Related Content
Similar to Underpinnings of the Interoperability Reference Architecture HISO 10040
Beating Bugs with Big Data: Harnessing HPC to Realize the Potential of Genomi...Tom Connor
Introducing the HPC challenges associated with developing a set of clinical microbial genomics services in the NHS in Wales. Demonstrating the potential of these technologies, and the impact it is already having for the patients of the Welsh NHS.
Content Modelling for VIEW Datasets Using ArchetypesKoray Atalag
This one also I presented at the HINZ conference.
ABSTRACT:
Use of health information for multiple purposes maximises its value. A good example is PREDICT, a clinical decision support system which has been used in New Zealand for a decade. Collected data are linked and enriched with a number of databases, including national collections, laboratory tests and pharmacy dispensing. We are proposing a new model-driven approach for data management based on openEHR Archetypes for the purpose of improving data linkage and future-proofing of data. The study looks at feasibility of building a content model for PREDICT - a methodology underpinning the Interoperability Reference Architecture. The main premise of the content model will be to provide a canonical model of health information which will be used to align incoming data from other data sources. With this approach it is possible to extend datasets without breaking semantics over long periods of time – a valuable capability for research. The content model was developed using existing archetypes from openEHR and NEHTA repositories. Except for two checklist type items, reused archetypes can faithfully represent the whole PREDICT dataset. The study also revealed we will need New Zealand specific extensions for demographic data. Use of archetype based content modelling can improve secondary use of clinical data.
The document discusses a project to analyze and predict sepsis early using clinical data. It aims to predict sepsis 6 hours before clinical diagnosis to allow for earlier treatment. The author handles missing data and class imbalance in a large dataset. Features are engineered and selected. Decision trees and XGBoost models are used for prediction, achieving partial success. Further research is needed on time-series modeling, feature importance, and model performance with a domain expert.
Implementation and Use of ISO EN 13606 and openEHRKoray Atalag
This was the prezo for the EMBC 2013 tutorial in Osaka, Japan. Intended for an introduction to the standards and technicalities and implementation of openEHR - which is the original formalism.
Big Data Analytics for Treatment Pathways John CaiJohn Cai
This document discusses using real-world big data analytics to understand treatment pathways. It begins by explaining the need for real-world evidence from real-world data to assess effectiveness and outcomes beyond randomized clinical trials. It then describes the volume, variety, and velocity characteristics of real-world big data from sources like claims, EMRs, surveys, and devices. Technical challenges of reconstructing complex patient journeys are discussed. Hadoop and MapReduce are presented as a potential solution by breaking the work into mappers that extract patient data and reducers that organize it into timelines. Examples are given of how this could enable cost, pathway, and outcomes analyses to better inform decision making.
TransCelerate is a nonprofit organization that aims to accelerate medical research by improving collaboration across the pharmaceutical industry. It has developed a Common Protocol Template (CPT) to standardize clinical trial protocols. The CPT provides a streamlined template for protocol content and format to make protocols easier to interpret, reduce complexity and costs, and enable automation. The CPT benefits various stakeholders by improving efficiency and quality. Its adoption by sponsors is valuable as it leverages industry expertise, supports compliance, and balances quality improvements with efficiency gains over time.
Impact Of a Clinical Decision Support Tool on Asthma Patients with Current As...Yiscah Bracha
The document summarizes research on the effect of computerized decision support (CDS) on the percentage of asthma patients with asthma action plans. The research found:
1) Implementation of a CDS tool at clinics led to increases in the percentage of pediatric patients with current asthma action plans, especially at clinics that previously lacked paper templates.
2) For adults, clinics that emphasized asthma action plans and had physicians start using the CDS tool saw increases, while clinics without paper templates saw physicians begin using the tool.
3) Statistical analysis showed the CDS tool had an initial positive effect at one pediatric clinic that oscillated over time, while having no significant effect at other clinics, possibly due to pre-existing tendencies of physicians to
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
openEHR Approach to Detailed Clinical Models (DCM) Development - Lessons Lear...Koray Atalag
Presented at Health Informatics New Zealand (HINZ 2017) Conference, 1-3 Nov 2017, Rotorua, New Zealand. Based on my Masters student Peter Wei's research. Authorship: Ping-Cheng Wei, Koray Atalag and Karen Day from the University of Auckland.
Health research, clinical registries, electronic health records – how do they...Koray Atalag
This is a talk I gave at my own organisation - National Institute for Health Innovation (NIHI) of the University of Auckland on 6 Aug 2014. Abstract as follows:
In this talk I’ll first cover the topic of clinical registry – an invaluable tool for supporting clinical practice but also gaining momentum in research and quality improvement. NIHI has been very active in this space: we have delivered the prestigious and highly successful National Cardiac Registry (ANZACS-QI) together with VIEW research team and also very recently launched the Gestational Diabetes Registry with Counties Manukau DHB & Diabetes Projects Trust. A few others are in likely to come down the line. This is a huge opportunity for health data driven research and NIHI to position itself as ‘the health data steward’ in the country given our independent status and existing IT infrastructure and “good culture” of working with health data . NIHI’s ‘health informatics’ twist in delivering these projects is how we go about defining ‘information’ – using a scientifically credible and robust methodology: openEHR. This is an international (and now national too) standard to non-ambiguously define health information so that they are easy to understand and also are computable. We build software (even automatically in some cases!) using models created by this formalism. I’ll give basics of openEHR approach and then walk you through how to make sense out of all these. Hopefully you may have an idea about its ‘value proposition’ (as business people call) or Science merit as I like to call it ;)
Michele Tonutti - Scaling is caring - Codemotion Amsterdam 2019Codemotion
A key challenge we face at Pacmed is quickly calibrating and deploying our tools for clinical decision support in different hospitals, where data formats may vary greatly. Using Intensive Care Units as a case study, I’ll delve into our scalable Python pipeline, which leverages Pandas’ split-apply-combine approach to perform complex feature engineering and automatic quality checks on large time-varying data, e.g. vital signs. I’ll show how we use the resulting flexible and interpretable dataframes to quickly (re)train our models to predict mortality, discharge, and medical complications.
This document summarizes a presentation about identifying deficiencies in long-term condition management using electronic medical records. It discusses using data mining of electronic medical records to analyze hypertension management and electronic referrals. Case studies show opportunities for improved monitoring and treatment of long-term conditions were identified. The presentation encourages using available electronic health record data to help improve healthcare processes and outcomes.
Ms. Drury outlines the EHR world for these Davies Winners before ARRA and the EHR Incentive Program existed, sharing the environment and the motivation for these privately owned physician practices who have been recognized by HIMSS as Davies Ambulatory Award Winners. The HIMSS Nicholas E. Davies Award of Excellence recognizes excellence in the implementation and use of health information technology, specifically electronic health records (EHRs), for healthcare organizations, independent physician practices and public health systems. The HIMSS process of evaluating applications from these practices and validating the use and value of HIT is rigorous for the applicants and for the HIMSS Ambulatory Award Committee.
The document discusses an open-source electronic health record (EHR) system called Oscar and describes its architecture and features. It provides examples of how Oscar has been used in radiotherapy settings and primary care clinics. The document also discusses a personal health record (PHR) module called MyOSCAR that is integrated with Oscar. MyOSCAR allows patients to access and share their health records. Two pilot studies are summarized that examine the use of MyOSCAR for blood pressure management and collecting drug safety data from patients. The studies found high completion rates of tasks in MyOSCAR and positive feedback from patients wishing to continue using the application.
"How do Professional Record Standards Support Timely Communication & Information Flows for all Participants in Health & Social Care"? Gurminder khamba (Clinical Lead for Secondary Care, HSCIC) discusses this question at the Healthcare Efficiency Through Technology Expo 2013.
Similar to Underpinnings of the Interoperability Reference Architecture HISO 10040 (20)
The Diabetes Discovery Project at Austin Health aimed to use their Cerner EMR system to routinely test HbA1c levels on inpatients over 54 to identify undiagnosed and poorly controlled diabetes. Testing of over 5,000 patients found 5% had undiagnosed diabetes and 29% had known diabetes. Higher HbA1c levels were associated with increased hospital admissions and longer lengths of stay for surgical patients. The project demonstrated using health IT to identify diabetes management opportunities. Ongoing work includes refining protocols and expanding to other patient populations.
This document summarizes a presentation on using data and informatics to improve allied health services. It discusses the history of allied health and challenges with data collection. Examples are provided of projects in New Zealand that used data to enhance patient and clinician experiences, reduce hospital-acquired infections, and inform staffing needs. The presentation emphasizes standardizing data to facilitate benchmarking and applying knowledge gained from data analysis to drive improvements in allied health.
This document presents a proof of concept for using Twitter data to conduct syndromic surveillance for public health monitoring. It analyzed tweets containing the keyword "measles" between 2014-2015 and found 1,408 relevant tweets. The number of tweets mentioning measles was compared to confirmed measles cases from a national surveillance system, showing potential for Twitter data as an early warning system. However, limitations include using a single keyword and the free Twitter API. Future work proposed improving data collection, applying machine learning techniques, and validating tweets with other health data sources.
The document discusses using surface modelling and mapping techniques to analyze healthcare data. It provides three scenarios as examples: 1) Mapping KPIs regionally to identify opportunities for improvement, 2) Mapping data around a specific pharmacy to examine market penetration, and 3) Comparing the market penetration of two smoking cessation medications. Surface mapping allows easy visualization and comparison of multiple data layers, helps protect patient privacy, and can provide insights into how to optimize outcomes.
The document summarizes how providing laptop computers to clinicians in a community allied health service has enhanced clinical care. Each of the 20 clinicians was provided a laptop with mobile data and remote desktop access to complete administrative and electronic tasks in the community rather than returning to the office. This has increased efficiency by allowing timely and collaborative work, which has decreased stress on clinicians and allowed for more timely information sharing with children and families. Some challenges remain around the weight of laptops and continuing reliance on paper records. Future plans include providing iPads and moving to more paperless systems.
This document describes the development of an electronic workflow system called scope to improve surgical practice at a District Health Board (DHB) hospital. The goals were to seamlessly map the patient journey, accurately collect coded data, and leverage trusted data to inform clinicians. The system streamlines waiting lists, captures accurate operating notes, and facilitates morbidity and mortality meetings. Implementation across surgical specialties has achieved good compliance and uptake. Preliminary results found increased quality of notes, discussion of complications, and potential to change practice through advanced data analysis. In conclusion, scope has replaced a disconnected paper system with a seamless electronic solution that fully captures standardized data to improve surgical outcomes.
1. The document discusses how healthcare has progressed beyond just electronic medical records (EMRs) and is now focused on areas like mobile computing, health collaboration, cloud-based back office systems, health intelligence, and clinical grade communications.
2. It provides examples of how technology is enabling cross-campus collaboration, telehealth, clinical collaboration using medical devices and teleradiology, and clinical communications.
3. The document advocates for sustainable eHealth innovation beyond just EMRs and discusses how areas like health analytics, mobility for care, patient-centered care, and emerging technologies can further improve healthcare.
The document discusses empowering healthcare through technology that is safe, works for everyone, and leaves no one behind. It describes how digital technologies are disrupting traditional healthcare models and outlines opportunities to enhance patient and provider experiences through virtual care, remote monitoring, and analytics. Key goals are mentioned like reducing readmissions, increasing effectiveness, and improving clinical productivity. The future of healthcare is envisioned as personalized, connected, data-driven, and empowering every person and organization to achieve more through technology.
The document discusses using analytics and care coordination to reduce hospitalizations and arrests of mental health patients. It notes that around 10% of patients are readmitted to psychiatric hospitals within 30 days of discharge. Care coordination aims to break this cycle through improved outcomes, treatment adherence, continuity of care, and identifying high-risk patients. Analytics tools can provide predictive modeling, population clustering, and care quality analysis to develop insights. The goal is to engage all stakeholders to deliver an integrated care plan through data-driven insights and coordination between providers.
Dr Nic Woods discusses tools for early recognition and management of sepsis using the electronic medical record (EMR). Sepsis poses a major global health challenge and burden. Tools discussed include a sepsis predictive model built into the EMR that can detect signs of sepsis with sensitivities of 68-91% and specificities of 91-97.6%. Clinical decision support and workflows in the EMR are also used to alert clinicians and guide treatment. Evaluations found these tools helped reduce mortality from sepsis by 4.2-17% and lower length of hospital stays. Key points emphasized that predictive models integrated into clinical workflows can positively impact outcomes, but more progress is still needed.
This document discusses allied health professionals and their role in the healthcare system. It lists various allied health roles and describes how they rehabilitate and enable patients by taking a collaborative and holistic approach focused on patient needs. The document emphasizes that allied health professionals help reduce health service needs by facilitating patients' independence and ability to remain in their communities. It argues that capturing allied health data can help provide visibility into their services, allow for quality improvement, and ultimately benefit patients through a more coordinated system where the "right intervention" is delivered at the "right time". The challenges of engaging stakeholders and integrating passive data extraction are also addressed.
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Gastrointestinal Infections
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The treatment consists of replacing lost liquids and electrolytes (drinking drinking water and other recommended liquids, including consumption of juicy fruits such as papayas, apples, pears, among others that contain water in their composition).
To prevent this, it is necessary to promote health education, improve the hygienic-sanitary conditions of markets and communities in general as a way of promoting, preserving and prolonging PUBLIC HEALTH.
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PGx Analysis in VarSeq: A User’s PerspectiveGolden Helix
Since our release of the PGx capabilities in VarSeq, we’ve had a few months to gather some insights from various use cases. Some users approach PGx workflows by means of array genotyping or what seems to be a growing trend of adding the star allele calling to the existing NGS pipeline for whole genome data. Luckily, both approaches are supported with the VarSeq software platform. The genotyping method being used will also dictate what the scope of the tertiary analysis will be. For example, are your PGx reports a standalone pipeline or would your lab’s goal be to handle a dual-purpose workflow and report on PGx + Diagnostic findings.
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Discuss and demonstrate the approaches with array and NGS genotyping methods for star allele calling to prep for downstream analysis.
Following genotyping, explore alternative tertiary workflow concepts in VarSeq to handle PGx reporting.
Moreover, we will include insights users will need to consider when validating their PGx workflow for all possible star alleles and options you have for automating your PGx analysis for large number of samples. Please join us for a session dedicated to the application of star allele genotyping and subsequent PGx workflows in our VarSeq software.
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Underpinnings of the Interoperability Reference Architecture HISO 10040
1. Underpinnings of the
Interoperability Reference
Architecture
(HISO 10040)
Koray Atalag1, Alastair Kenworthy2, David Hay3
1.NIHI – University of Auckland
2.Ministry of Health
3.Orion Health
2.
3. The Problem
• Patient centred integrated/shared care paradigms
hinge on more interconnectivity
• We all know about silos: 1+1 >2 when shared
• It’s all about People, processes and technology
• Standards crucial – but need an overarching framework
– No one size fits all! depends on needs, resources
– Myriad of standards, methods etc.
– Not so much success so far worldwide
• Narrow opportunity window in NZ to enable sector-
wide consistency & interoperability
(too many projects in-early flight or kicking off)
4. State of the world
• US: advanced provider-centric systems but little inter-
connectivity (HL7 v2/CDA)
• Canada: CHI providing leadership & standards
(v2/v3/CDA)
• UK: bootstrapping from CfH disaster, focus on high
value/established systems (HL7/13606)
• Nordic: well established, (↑13606 / HL7 v2/CDA)
• EU: very patchy – HL7/↑13606/openEHR
• Asia: patchy -propriety / HL7 / little 13606/openEHR
• Brazil/Paraguay: mainly openEHR & HL7 v2/CDA
• Australia: Nehta/PCEHR, v2/v3/CDA & openEHR
5. State of the nation
• Core EHR by 2014 – are we getting there?
• National planning, regional implementations
• Shared Care and PrimarySecondary
– Shared care projects: long term conditions, maternity,
well child etc.
• Clinical Data Repository (CDR) as enabler
– GP2GP, Transfer of Care, eMedications
– Medicines reconciliation, specialist CIS
– Others: NZULM, new NHI/HPI
• Good emphasis & support for standards
6. The Principles
1. Align to national strategy: as per national and regional plans
2. Invest in information: use a technology agnostic common
content model, and use standard terminologies
3. Use single content model: information for exchange will be
defined and represented in a single consistent way
4. Align to business needs: prioritise the Reference Architecture
in line with regional and national programmes
5. Work with sector: respect the needs of all stakeholders
6. Use proven standards: adopt suitable and consistent national
and international standards wherever they exist (in preference to
inventing new specifications)
7. Use a services approach: move the sector from a messaging
style of interaction to one based on web services
9. What is ECM?
• IT IS A REFERENCE LIBRARY - for enabling consistency in HIE
Payload
• Superset of all clinical dataset definitions
– normalised using a standard EHR record organisation (aka DCM)
– Expressed as reusable and computable models – Archetypes
• Top level organisation follows CCR*
• Further detail provided by:
– Existing relevant sources (CCDA, Nehta, epSoS, HL7 FHIR etc.)
– Extensions (of above) and new Archetypes (NZ specific)
• Each HIE payload (CDA) will correspond to a subset (and
conform)
* kind of – CCDA may be more appropriate
12. ECM Working Principle
Exchange Content Model
Conforms to
Message
Payload
(CDA)
Source System Recipient System
Map Map
Source to Web Service ECM to
ECM Recipient
Exchange
Data
Object
Source data Recipient data
13. Authoring & HISO process
• Initiated & funded by Health Sector Architects Group
(SAG), an advisory group to the NHITB
• 4 co-authors – from Interoperability WG
• Initial feedback from SAG then publish on HIVE
• ABB produced - condensed version of IRA (2011)
• Public comment and evaluation panel October 2011
• Ballot round February 2012
• Interim standard April 2012
• Trial implementation with Northern DHBs, 2012/13
14. Archetypes
• The way to go for defining clinical content
CIMI (led by S. Huff @ Intermountain & Mayo)
In many nat’l programmes (eg. Sweden, Slovenia, Australia, Brazil)
• Smallest indivisible units of clinical information with clinical context
• Brings together building blocks from Reference Model (eg. record
organisation, data structures, types)
• Puts constraints on them:
– Structural constraints (List, table, tree, clusters)
– What labels can be used
– What data types can be used
– What values are allowed for these data types
– How many times a data item can exist?
– Whether a particular data item is mandatory
– Whether a selection is involved from a number of items/values
15. Logical building blocks of EHR
EHR
Folders
Compositions
Sections
Entries
Clusters
Elements
Data values
17. Extending ECM
• Addition of new concepts
• Making existing concepts more specific
– powerful Archetype specialisation mechanism:
– Lab result > HbA1C result, Lipid profiles etc.
Problem First level specialisation
Text or Coded Term Diagnosis Second level specialisation
Clinical description
Date of onset Coded Term Diabetes
Date of resolution + diagnosis
No of occurrences Grading +
Diagnostic criteria Diagnostic criteria
Stage Fasting > 6.1
GTT 2hr > 11.1
Random > 11.1
19. Case Study: Medication
• Essential to get it right – first in patient safety!
• Single definition of Medication will be reused in many
places, including:
– ePrescribing
– My List of Medicines
– Transfer of care
– Health (status & event) summary
– Specialist systems
– Public Health / Research
• Currently no standard def in NZ
(coming soon 10043 Connected Care)
• NZMT / NZULM & Formulary > bare essentials
20. Current state & projects
• PMS: each vendor own data model
• GP2GP: great start for structure
• NZePS: started with propriety model, now waiting
for standard CDA.
– PMS vendors implementing Toolkit based Adapter
• Hospitals: some using CSC MedChart
• Pharmacies?
• Others?
Actually we’re not doing too bad
21. Why bother?
(with a standard structured Medication definition)
“If you think about the seemingly simple concept of
communicating the timing of a medication, it readily
becomes apparent that it is more complex than most
expect…”
“Most systems can cater for recording ‘1 tablet 3 times a
day after meals’, but not many of the rest of the
following examples, ...yet these represent the way
clinicians need to prescribe for patients...”
Dr. Sam Heard
22. Medication timing
Dose frequency Examples
every time period …every 4 hours
n times per time period …three times per day
n per time period …2 per day
…6 per week
every time period range …every 4-6 hours,
…2-3 times per day
Maximum interval …not less than every 8 hours
Maximum per time period …to a maximum of 4 times per
day
Acknowledgement: Sam Heard
23. Medication timing cont.
Time specific Examples
Morning and/or lunch and/or …take after breakfast and
evening lunch
Specific times of day 06:00, 12:00, 20:00
Dose duration
Time period …via a syringe driver over 4
hours
Acknowledgement: Sam Heard
24. Medication timing cont.
Event related Examples
After/Before event …after meals
…before lying down
…after each loose stool
…after each nappy change
n time period before/after …3 days before travel
event
Duration n time period …on days 5-10 after
before/after event menstruation begins
Acknowledgement: Sam Heard
25. Medication timing – still cont.
Treatment duration Examples
Date/time to date/time 1-7 January 2005
Now and then repeat after n …start, repeat in 14 days
time period/s
n time period/s …for 5 days
n doses …Take every 2 hours for 5 doses
Acknowledgement: Sam Heard
26. Medication timing – even more!
Triggers/Outcomes Examples
If condition is true …if pulse is greater than 80
…until bleeding stops
Start event …Start 3 days before travel
Finish event …Apply daily until day 21 of
menstrual cycle
Acknowledgement: Sam Heard
27. Modelling Medication Definition
• NZePS data model (v1.9) & draft 10043
Connected Care CDA templates
• Start from Nehta ePrescribing model
– Analyse models and match data elements
– Extend where necessary as per NZ requirements
• Add new items or rename existing
• Tighter constrains on existing items (e.g.
cardinality, code sets, data types)
31. Results & Outlook
• Extended model 100% covering NZePS
(community ePrescribing)
• Must consider secondary care
• Need to look in more detail:
– Consolidated CDA
– epSoS (European framework)
– Other nat’l programmes
• Generate Payload CDA using transforms
32. Value Proposition
• Content is ‘clinician’s stuff’ – not techy; yet most existing standards are
meaningless for clinicians and vice versa for techies
– Archetypes in ‘clinical’ space – easily understood & authored by them
• Single source of truth for entire sector
– One agreed way of expressing clinical concepts – as opposed to
multiple ways of doing it with HL7 CDA (CCDA is a good first step)
• Archetypes can be transformed into numerous formats – including CDA
• Archetypes are ‘maximal datasets’
– Much easier to agree on
• Scope not limited to HIE but whole EHR; workflow supported
• ECM principle invest in information fulfilled completely
– future proof content today for tomorrow’s implementation technology
(e.g. FHIR etc., distributed workflows etc.)
33. Thank you – Questions?
Empowered by openEHR - Clinicians in the Driver’s Seat!
Editor's Notes
These are the three building blocks – or pillars – of the HISO 10040 series that embodies the central ideas of the Reference Architecture for Interoperability10040.1 is about regional CDRs and transport10040.2 is about a content model for information exchange, shaped by the generic information model provided by CCR, with SNOMED as the default terminology, and openEHR archetypes as the chief means of representation10040.3 is about CDA structured documents as the common currency of exchange – not every single transaction type, but the patient information-laden ones
Published by HISO (2012); Part of the Reference Architecture for Interoperability“To create a uniform model of health information to be reused by different eHealth Projects involving HIE”Consistent, Extensible, Interoperable and Future-Proof Data
Content is ‘clinician’s stuff’ – not techy; yet most existing standards are meaningless for clinicians and vice versa for techiesopenEHR Archetypes are in ‘clinical’ space – easily understood and authored by themArchetypes can be transformed into numerous formats – including CDAArchetypes are ‘maximal datasets’ e.g. They are much more granular than other models when needed. Support more use cases – indeed almost anything to do with EHR (including some workflow). Scope not limited to HIE but whole EHR.One agreed way of expressing clinical concepts – as opposed to multiple ways of doing it with HL7 CDA (CCDA is a good first step though)ECM invest in information fulfilled completely – future proof technology today with ECM for tomorrow’s implementation technology (e.g. FHIR etc., distributed workflows etc.)
... And more
... And more
... And more
Objective of this demo is to show the bottom-up content development approach.Certain Archetypes shared by key HIE (eRef, ePrescribing, PREDICT) undergo an iterative localisation processInternational > Multiple Local projects (added & extended) > Added to ECM