This is the prezo I presented at HINZ 2014 conference.
Gestational diabetes has implications for both mother and child with risk of complications during pregnancy, and type 2 diabetes later in life. This paper presents the initial lessons learned from the development of a clinical registry. The aims of the Registry are: 1) 100% successful diabetes screening within 3 months of delivery; 2) Annual type 2 diabetes screening; 3) Early warning in subsequent pregnancies.
We have employed the openEHR standard which underpins our national interoperability reference architecture to represent the dataset and also to build the web-based registry system. Use of this rigorous methodology to tackle health information is expected to ensure semantic consistency of Registry data and maximise interoperability with other Sector projects. The development work has been facilitated by the ability to transform the dataset automatically into software code – ensuring clinical requirements accurately translated into technical terms.
Dataset has been finalised, registry system has been developed and deployed for pilot implementation. Data entry is underway for participants after consenting.
This registry is expected to increase the screening of women leading to earlier detection of diabetes. It should provide a valuable picture of the condition and is intended for extension and wider roll-out after evaluation.
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.
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...Koray Atalag
This is the prezo I have at the Australasian Long-Term Conditions Conference in Auckland on 30 Jul 2014. Focus was on prevention and management of long term conditions and use of clinical registries has proven to be effective. This is a pilot project at a large healthcare provider organisation in Auckland (Counties Manukau District Health Board) where we used the full openEHR stack to build web based front end with the OceanEHR backend.
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 ;)
The Inferscience introduce Infera, a clinical decision support engine that improves decision making, assisting clinicians to work more quick-witted. In this presentation, you can get the detailed information about this Advanced Clinical Decision Support System.
The Pre-Anesthesia Evaluation Module is designed to manage the data and workflow of pre-anesthesia evaluation, either at the pre-admission testing visit or at the surgeon’s office. Medical history is collected from patients via a self-administered Tablet questionnaire, and available data regarding that patient is also downloaded from the EHR. This data is used to determine what testing is needed prior to anesthesia. This system can be used in the surgeon’s office, to help avoid anesthesia complications and help prevent canceled or delayed cases. A set of screenshots and an overview of the module can be reviewed via this downloadable PowerPoint presentation.
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.
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...Koray Atalag
This is the prezo I have at the Australasian Long-Term Conditions Conference in Auckland on 30 Jul 2014. Focus was on prevention and management of long term conditions and use of clinical registries has proven to be effective. This is a pilot project at a large healthcare provider organisation in Auckland (Counties Manukau District Health Board) where we used the full openEHR stack to build web based front end with the OceanEHR backend.
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 ;)
The Inferscience introduce Infera, a clinical decision support engine that improves decision making, assisting clinicians to work more quick-witted. In this presentation, you can get the detailed information about this Advanced Clinical Decision Support System.
The Pre-Anesthesia Evaluation Module is designed to manage the data and workflow of pre-anesthesia evaluation, either at the pre-admission testing visit or at the surgeon’s office. Medical history is collected from patients via a self-administered Tablet questionnaire, and available data regarding that patient is also downloaded from the EHR. This data is used to determine what testing is needed prior to anesthesia. This system can be used in the surgeon’s office, to help avoid anesthesia complications and help prevent canceled or delayed cases. A set of screenshots and an overview of the module can be reviewed via this downloadable PowerPoint presentation.
Paul Coplan, VP, Johnson & Johnson_mHealth IsraelLevi Shapiro
Pesentation, October 19th, 2021: What’s Next in RWE for Medical Devices: The Art of the Possible. Presented by Paul Coplan, ScD, MBA, FISPE, Vice President, Med Device Epidemiology and RWD Sciences, Johnson & Johnson; Adjunct Professor, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine; Fellow of the International Society of Pharmacoepidemiology
- Why RWE is Important for Medical Devices: Challenges with Clinical Trials of Medical Devices (Blinding, Surgeon skill/technique, Hospital process, Product modifications, Long term Follow up, Enrolment challenges)
- Types of Real-World Data Sources (Complaints like MAUDE, Eudramed and Company Databases, Hospital Databases, Electronic Health Records, Claims, Registries, Patient surveys, Surgeon surveys, PROs, Patient Preferences, wearables, sensors, social media, Surgical videos, device generated data, radiographic images)
- FDA CDRH Report on RWE Examples for Regulatory Decisions
- J&J Med Device Epidemiology & Real-World Data Sciences
- US National Evaluation System for Health Technology (NEST)
- RWE for Safety Assessments: Cobalt in Implants and at Work and Risk of Cancer
- Summary of Cobalt Exposure and All-Site Cancer Risk, by Study Type
- Comparative Effectiveness Studies Using RWE
- Summary
a. Use of RWE is important to benefit patients globally and enhance the safety and innovation of medical devices
b. Regulators are interested in using RWE for regulatory decisions but data quality and evidence needs to be regulatory grade
c. NEST has been a useful forum to advance the use of RWE for regulatory decisions in the US
d. RWE can be used for safety assessments, regulatory decisions, comparative effectiveness research, and R&D of products
Precise Patient Registries for Clinical Research and Population ManagementDale Sanders
Patient registries have evolved from external, mandatory reporting databases to playing a critical role in internal clinical research, clinical quality, cost reduction, and population health management. This slide deck describes how to design those precise registries.
Paul Aylin, Co-Director of the Dr Foster Unit at Imperial College London, gives concrete examples of using a specific statistical model for monitoring care quality, cumulative sum (CUSUM).
Current clinical electronic health record systems do not provide accessible information for quality assurance and research purposes. Furthermore, data entry is limited due to inappropriate and/or insufficient fields.
Evaluating new models of care: Improvement Analytics UnitNuffield Trust
Martin Caunt, Improvement Analytics Unit Project Director and NHS England and Adam Steventon, Director of Data Analytics at The Health Foundation share insights into how they have approached evaluating new models of care.
Implementing American Heart Association Practice Standards for Inpatient ECG ...Allina Health
Implementing American Heart Association Practice Standards for Inpatient ECG Monitoring: An Interventional Study at Abbott Northwestern Hospital presented by Kristin Sandau, PhD, RN
Ronan Herlihy on Engaging Clinicians with data on their ordering practicesCirdan
The appropriate ordering project uses data extracted from Electronic Medical Record to create dashboards to inform and engage clinicians in ordering practices. This presentation looks at the techniques used to create answers for the clinicians questions and discusses the purpose behind 12 dashboards. It looks at the change management approaches and challenges.
The initial pilot project has been embraced by a number of local health districts in NSW and templates have been made available along with training tools.
Martin Utley, Director of the Clinical Operational Research Unit at University College London, reflects upon his involvement in the launch of specific tools to monitor care quality for paediatric cardiac surgery.
A standards-based approach to development of clinical registries - Initial lessons learnt from the gestational diabetes registry. Presented by Koray Atalag, National Institute for Health Innovation, University of Auckland, at HINZ 2014, 12 November 2014, 12pm, Plenary Room 2
Paul Coplan, VP, Johnson & Johnson_mHealth IsraelLevi Shapiro
Pesentation, October 19th, 2021: What’s Next in RWE for Medical Devices: The Art of the Possible. Presented by Paul Coplan, ScD, MBA, FISPE, Vice President, Med Device Epidemiology and RWD Sciences, Johnson & Johnson; Adjunct Professor, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine; Fellow of the International Society of Pharmacoepidemiology
- Why RWE is Important for Medical Devices: Challenges with Clinical Trials of Medical Devices (Blinding, Surgeon skill/technique, Hospital process, Product modifications, Long term Follow up, Enrolment challenges)
- Types of Real-World Data Sources (Complaints like MAUDE, Eudramed and Company Databases, Hospital Databases, Electronic Health Records, Claims, Registries, Patient surveys, Surgeon surveys, PROs, Patient Preferences, wearables, sensors, social media, Surgical videos, device generated data, radiographic images)
- FDA CDRH Report on RWE Examples for Regulatory Decisions
- J&J Med Device Epidemiology & Real-World Data Sciences
- US National Evaluation System for Health Technology (NEST)
- RWE for Safety Assessments: Cobalt in Implants and at Work and Risk of Cancer
- Summary of Cobalt Exposure and All-Site Cancer Risk, by Study Type
- Comparative Effectiveness Studies Using RWE
- Summary
a. Use of RWE is important to benefit patients globally and enhance the safety and innovation of medical devices
b. Regulators are interested in using RWE for regulatory decisions but data quality and evidence needs to be regulatory grade
c. NEST has been a useful forum to advance the use of RWE for regulatory decisions in the US
d. RWE can be used for safety assessments, regulatory decisions, comparative effectiveness research, and R&D of products
Precise Patient Registries for Clinical Research and Population ManagementDale Sanders
Patient registries have evolved from external, mandatory reporting databases to playing a critical role in internal clinical research, clinical quality, cost reduction, and population health management. This slide deck describes how to design those precise registries.
Paul Aylin, Co-Director of the Dr Foster Unit at Imperial College London, gives concrete examples of using a specific statistical model for monitoring care quality, cumulative sum (CUSUM).
Current clinical electronic health record systems do not provide accessible information for quality assurance and research purposes. Furthermore, data entry is limited due to inappropriate and/or insufficient fields.
Evaluating new models of care: Improvement Analytics UnitNuffield Trust
Martin Caunt, Improvement Analytics Unit Project Director and NHS England and Adam Steventon, Director of Data Analytics at The Health Foundation share insights into how they have approached evaluating new models of care.
Implementing American Heart Association Practice Standards for Inpatient ECG ...Allina Health
Implementing American Heart Association Practice Standards for Inpatient ECG Monitoring: An Interventional Study at Abbott Northwestern Hospital presented by Kristin Sandau, PhD, RN
Ronan Herlihy on Engaging Clinicians with data on their ordering practicesCirdan
The appropriate ordering project uses data extracted from Electronic Medical Record to create dashboards to inform and engage clinicians in ordering practices. This presentation looks at the techniques used to create answers for the clinicians questions and discusses the purpose behind 12 dashboards. It looks at the change management approaches and challenges.
The initial pilot project has been embraced by a number of local health districts in NSW and templates have been made available along with training tools.
Martin Utley, Director of the Clinical Operational Research Unit at University College London, reflects upon his involvement in the launch of specific tools to monitor care quality for paediatric cardiac surgery.
A standards-based approach to development of clinical registries - Initial lessons learnt from the gestational diabetes registry. Presented by Koray Atalag, National Institute for Health Innovation, University of Auckland, at HINZ 2014, 12 November 2014, 12pm, Plenary Room 2
1Running Head Research Paper Final Draft6Research Paper.docxaulasnilda
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Running Head: Research Paper Final Draft
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Research Paper Final Draft
Research Paper Final Draft
Himaswetha Polavarapu
Dr.Mary Cecil
University Of The Cumberlands
Information Governance
12/01/2019
ABSTRACT
One of major issues in todays hospitals is period for which medical records are to be retained. Therefore health information managements professionals have traditionally performed record retention and also the destruction functions using media, including the paper, images, the optical disk, microfilm, the DVD, and also CD-ROM. Health information managements departments therefore has to maintain specific program in order to retain and also destruct records. The main purpose of this paper to investigate and maintain the retention and also destruction process of the medical records in hospitals and codifying appropriate guidelines. The research is conducted as cross-sectional descriptive study in hospitals in India. Data was collected using the Check List. Viewpoints to be obtained using Delphi technique. Data entry and also the statistical analysis are performed using the SPSS.
INTRODUCTION
Due to many practices and services offered to people in healthcare that cater to the basic needs of an individual, the company undergoes a series of changes in record overtime which are retained safely to avoid them landing into unauthorized hands because some documents may be carrying sensitive information about individuals. Record retention involves storing records that are not in use anymore for example marriage certificates. Because of this need, different companies have developed an online policy of record detention that will determine how long should these records be retained and provide a disposal guideline. In my research, I will analyze online policies developed by the Healthcare industry on the management of their record retention.
BACKGROUND
Record retention is a very important step initiated in healthcare to ensure there is continuity of care for a patient. Professionals traditionally have been maintaining records through different means like using media as well as paper from which it can be retrieved when the owner visits the healthcare unit again thus can be used for time reference. The management has established an online policy through an appropriate retention schedule which will ensure there is minimal or no legal discovery of the records detained, this approach has worked positively in many organizations including the healthcare sector. Advancement to an online system of record retention through technology has improved the management of this process where data can be retrieved from the system for a specific person very fast and securely according to (Kruse.et.al.2015).
LITERATURE REVIEW
Retention Policies
In the healthcare system, management of records involves some basic steps from creation to utilization to maintenance then finally to retention. The following guidelines are responsible for the development, managem ...
The COVID-19 pandemic introduced many weaknesses in the existing US healthcare system. However, it also created opportunities for innovation in technologies revolutionizing the healthcare industry. The mRNA vaccine was the most important innovation, improving patient care and changing healthcare forever. However, another technological advancement, the digital health passport, demonstrated the benefits emerging technologies can have in health. The controversy around a digital health passport is evident and leads to deeper discussions around health data privacy and innovation.
The digital health passport in many ways was just a Beta technology used to easily show required proof of vaccination. For many, it was a product of convenience. However, some certainly saw it as an invasion of privacy. What if there was the potential to vastly increase the functionality of such a “health passport” to include all the relevant points of your medical history while also ensuring that no person without permission could see that information?
This would involve an individual holding a portable health record on a digital wallet using blockchain technology. The portable health record would include all medications a patient is currently prescribed and taking, those he or she is allergic to, lab work results, and both prior and existing diagnoses. It could expand to hold a patient’s full medical record from birth, but in early stages some of the greatest advantages would be for those with multiple medications and/or chronic illnesses (Refer to Exhibit 1 for Image). Some may ask why this would provide any advantages considering Electronic Health Record (EHR) systems are becoming more widespread, hold vast amounts of data, and functionality is ever increasing.
The issues lie in the current EHR/EMR infrastructure within the United States. As of 2019 the three largest players, EPIC, Cerner, and Medtech, controlled nearly 70% of the hospital market. Each has built a vast network of partnerships and found ways to expand products and offerings. The result: an oligopoly made up of siloed, independent companies protecting their piece of the pie. This has created a major issue—lack of interoperability between players ...
> HTA and Real World Evidence (RWE)
> Why RWE? - Limitations with RCT
> RCT v/s RWE
> Definition of RWE
> Sources of RWE
> Advantages of RWE
> Application of Real World Data (RWD) in RWE
> Benefits of RWD in RWE
> Why Data Sharing is Important?
> Important Stakeholders
> How to Encourage Data Sharing?
> Benefits of Data Sharing
> Case Studies
> Data Privacy Scenario
> Data Security in India
> Regulatory Perspectives Around RWD/RWE
> Way Forward
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives.
In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed:
-How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests
-Examples of proposed solutions to real-life challenges AROs and hospitals often encounter
CORD Rare Drug Conference, June 8 - 9, 2022
Opportunities and Challenges for Data Management Real-World Data and Real-World Evidence
• Patient support programs: Sandra Anderson, Innomar Strategies
• AI for Data Management and Enhancement: Aaron Leibtag, Pentavere
• Patient Support and RWE: Laurie Lambert, CADTH
The Pistoia Alliance is examining the challenges of the Faster Safe Companion Diagnostics (CDx) by Aligning Discovery & Clinical Data in the Regulatory Domain.
The slides discuss whether the data standards used in the research environment be aligned better with the data standards used in the regulated environment? If so, the time and cost of the development of NGS-based CDx could be reduced.
The role of real world data and evidence in building a sustainable & efficien...Office of Health Economics
This presentation defines RWD and RWE in the context of digital health, and looks at potential uses for RWD and RWE. It briefly sets out the current landscape in Malaysia and looks at the challenges in using RWE. In particular, the issues of access, governance and ensuring good quality are considered.
In this presentation for Digital Health Institute Summit 2020 I will explain how we overcame barriers for patient engagement and achieved very high response rates using our ePRO ZEDOC Platform. I'll give real-world insights from a project we ran at the Rheumatology service at NUH in Singapore.
I wear two hats - this talk is with the first one!
Computational Model Discovery for Building Clinical Applications: an Example ...Koray Atalag
Presented at Health Informatics New Zealand (HINZ 2017) Conference, 1-3 Nov 2017, Rotorua, New Zealand. Based on my PhD student Dewan's research.
Authorship: Dewan Sarwar, Koray Atalag, David Nickerson
The University of Auckland
A Semantic Web based Framework for Linking Healthcare Information with Comput...Koray Atalag
Presented at Health Informatics New Zealand (HINZ 2017) Conference, 1-3 Nov 2017, Rotorua, New Zealand. Authorship: Koray Atalag, Reza Kalbasi, David Nickerson
The University of Auckland
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.
openEHR in Research: Linking Health Data with Computational ModelsKoray Atalag
My prezo at Medinfo 2017 openEHR Developers Workshop.
The aim was to demonstrate how openEHR supports very advanced research and analytics with examples from computational physiology and biosimulation to create patient-specific decision support.
Bringing Things Together and Linking to Health Information using openEHRKoray Atalag
My prezo at Medinfo 2015 Conference in the workshop:
Digital Patient Modeling and Clinical Decision Support by Kerstin Denecke, Stefan Kropf, Claire Chalopin, Mario A, Cypko, Yihan Deng, Jan Gaebel, Koray Atalag
SNOMED Bound to (Information) Model | Putting terminology to workKoray Atalag
Prezo I gave at the HL7 New Zealand FHIR and Ice Seminar (latter referring to SNOMED!). I was asked to talk briefly about how information models relate to terminology and also highlight some other information modelling formalisms and initiatives (e.g. openEHR, ISO/CEN 13606, CIMI and DICOM SR).
Clinical modelling with openEHR ArchetypesKoray Atalag
This is the prezo I used in CellML workshop in Waiheke Island, Auckland, New Zealand on 14 April 2015. The aim was to introduce information modelling with openEHR and how to achieve semantic interoperability by using shared ontologies and clinical terminology.
Linkages to EHRs and Related Standards. What can we learn from the Parallel U...Koray Atalag
This is the prezo I used during the CellML workshop in Waiheke Island, Auckland, New Zealand on 13 April 2015. The aim was to introduce information modelling methods and tools for the purpose of inspiring computational modelling work in the area of semantics and interoperability.
Information Models & FHIR --- It’s all about content!Koray Atalag
In this prezo I have touched upon what an information model is and what is not, especially with relation to terminology. The highlight is to demonstrate the similarities (and differences) between clinical models of openEHR (archetypes & templates) and FHIR. It is obvious that the World doesn't need more standards and a collaborative approach to content development is a necessity. Lastly I make connection with New Zealand's content model approach.
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.
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.
Underpinnings of the New Zealand Interoperability Reference ArchitectureKoray Atalag
This one I presented at the HINZ conference 7-9 Nov 2012 at Rotorua, New Zealand.
ABSTRACT:
As we are moving into new paradigms of care, sharing of health information becomes crucial. We need new systems and more interconnectivity to support this. The regional approach to eHealth solutions in New Zealand hinges on establishing trusted and interoperable systems. The Interoperability Reference Architecture is a first step towards providing overall principles and standards to reach this goal. A core group from the Sector Architects Group was formed and prepared the first draft of this document. After initial internal feedback it went through wider consultation – including public. Good feedback was received, including international. It then went through formal HISO processes and was approved as a national interim standard. The Reference Architecture comprises three pillars which define: 1) XDS based access to clinical data repositories, 2) a common content model underpinned by CCR and openEHR Archetypes to which all health information exchange should conform, and 3) use of CDA as common currency for payload. A trial implementation is yet to be conducted, however we used the Content Model to align ePrescribing data model with the Australian model in order to validate the methodology. The Reference Architecture will provide an incremental step-by-step implementation approach to interoperability and thus minimise risk.
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.
Why ICT Fails in Healthcare: Software Maintenance and MaintainabilityKoray Atalag
This presentation was for a SERG seminar at the University of Auckland Department of Computer Science. I present why software maintenance is a barrier for adoption of IT in healthcare and the maintainability aspects based on ISO/IEC 9126 software quality standard quality model. I then present the preliminary results of my research here.
CHAPTER 1 SEMESTER V - ROLE OF PEADIATRIC NURSE.pdfSachin Sharma
Pediatric nurses play a vital role in the health and well-being of children. Their responsibilities are wide-ranging, and their objectives can be categorized into several key areas:
1. Direct Patient Care:
Objective: Provide comprehensive and compassionate care to infants, children, and adolescents in various healthcare settings (hospitals, clinics, etc.).
This includes tasks like:
Monitoring vital signs and physical condition.
Administering medications and treatments.
Performing procedures as directed by doctors.
Assisting with daily living activities (bathing, feeding).
Providing emotional support and pain management.
2. Health Promotion and Education:
Objective: Promote healthy behaviors and educate children, families, and communities about preventive healthcare.
This includes tasks like:
Administering vaccinations.
Providing education on nutrition, hygiene, and development.
Offering breastfeeding and childbirth support.
Counseling families on safety and injury prevention.
3. Collaboration and Advocacy:
Objective: Collaborate effectively with doctors, social workers, therapists, and other healthcare professionals to ensure coordinated care for children.
Objective: Advocate for the rights and best interests of their patients, especially when children cannot speak for themselves.
This includes tasks like:
Communicating effectively with healthcare teams.
Identifying and addressing potential risks to child welfare.
Educating families about their child's condition and treatment options.
4. Professional Development and Research:
Objective: Stay up-to-date on the latest advancements in pediatric healthcare through continuing education and research.
Objective: Contribute to improving the quality of care for children by participating in research initiatives.
This includes tasks like:
Attending workshops and conferences on pediatric nursing.
Participating in clinical trials related to child health.
Implementing evidence-based practices into their daily routines.
By fulfilling these objectives, pediatric nurses play a crucial role in ensuring the optimal health and well-being of children throughout all stages of their development.
One of the most developed cities of India, the city of Chennai is the capital of Tamilnadu and many people from different parts of India come here to earn their bread and butter. Being a metropolitan, the city is filled with towering building and beaches but the sad part as with almost every Indian city
CHAPTER 1 SEMESTER V PREVENTIVE-PEDIATRICS.pdfSachin Sharma
This content provides an overview of preventive pediatrics. It defines preventive pediatrics as preventing disease and promoting children's physical, mental, and social well-being to achieve positive health. It discusses antenatal, postnatal, and social preventive pediatrics. It also covers various child health programs like immunization, breastfeeding, ICDS, and the roles of organizations like WHO, UNICEF, and nurses in preventive pediatrics.
Navigating Challenges: Mental Health, Legislation, and the Prison System in B...Guillermo Rivera
This conference will delve into the intricate intersections between mental health, legal frameworks, and the prison system in Bolivia. It aims to provide a comprehensive overview of the current challenges faced by mental health professionals working within the legislative and correctional landscapes. Topics of discussion will include the prevalence and impact of mental health issues among the incarcerated population, the effectiveness of existing mental health policies and legislation, and potential reforms to enhance the mental health support system within prisons.
CRISPR-Cas9, a revolutionary gene-editing tool, holds immense potential to reshape medicine, agriculture, and our understanding of life. But like any powerful tool, it comes with ethical considerations.
Unveiling CRISPR: This naturally occurring bacterial defense system (crRNA & Cas9 protein) fights viruses. Scientists repurposed it for precise gene editing (correction, deletion, insertion) by targeting specific DNA sequences.
The Promise: CRISPR offers exciting possibilities:
Gene Therapy: Correcting genetic diseases like cystic fibrosis.
Agriculture: Engineering crops resistant to pests and harsh environments.
Research: Studying gene function to unlock new knowledge.
The Peril: Ethical concerns demand attention:
Off-target Effects: Unintended DNA edits can have unforeseen consequences.
Eugenics: Misusing CRISPR for designer babies raises social and ethical questions.
Equity: High costs could limit access to this potentially life-saving technology.
The Path Forward: Responsible development is crucial:
International Collaboration: Clear guidelines are needed for research and human trials.
Public Education: Open discussions ensure informed decisions about CRISPR.
Prioritize Safety and Ethics: Safety and ethical principles must be paramount.
CRISPR offers a powerful tool for a better future, but responsible development and addressing ethical concerns are essential. By prioritizing safety, fostering open dialogue, and ensuring equitable access, we can harness CRISPR's power for the benefit of all. (2998 characters)
Global launch of the Healthy Ageing and Prevention Index 2nd wave – alongside...ILC- UK
The Healthy Ageing and Prevention Index is an online tool created by ILC that ranks countries on six metrics including, life span, health span, work span, income, environmental performance, and happiness. The Index helps us understand how well countries have adapted to longevity and inform decision makers on what must be done to maximise the economic benefits that comes with living well for longer.
Alongside the 77th World Health Assembly in Geneva on 28 May 2024, we launched the second version of our Index, allowing us to track progress and give new insights into what needs to be done to keep populations healthier for longer.
The speakers included:
Professor Orazio Schillaci, Minister of Health, Italy
Dr Hans Groth, Chairman of the Board, World Demographic & Ageing Forum
Professor Ilona Kickbusch, Founder and Chair, Global Health Centre, Geneva Graduate Institute and co-chair, World Health Summit Council
Dr Natasha Azzopardi Muscat, Director, Country Health Policies and Systems Division, World Health Organisation EURO
Dr Marta Lomazzi, Executive Manager, World Federation of Public Health Associations
Dr Shyam Bishen, Head, Centre for Health and Healthcare and Member of the Executive Committee, World Economic Forum
Dr Karin Tegmark Wisell, Director General, Public Health Agency of Sweden
The dimensions of healthcare quality refer to various attributes or aspects that define the standard of healthcare services. These dimensions are used to evaluate, measure, and improve the quality of care provided to patients. A comprehensive understanding of these dimensions ensures that healthcare systems can address various aspects of patient care effectively and holistically. Dimensions of Healthcare Quality and Performance of care include the following; Appropriateness, Availability, Competence, Continuity, Effectiveness, Efficiency, Efficacy, Prevention, Respect and Care, Safety as well as Timeliness.
Medical Technology Tackles New Health Care Demand - Research Report - March 2...pchutichetpong
M Capital Group (“MCG”) predicts that with, against, despite, and even without the global pandemic, the medical technology (MedTech) industry shows signs of continuous healthy growth, driven by smaller, faster, and cheaper devices, growing demand for home-based applications, technological innovation, strategic acquisitions, investments, and SPAC listings. MCG predicts that this should reflects itself in annual growth of over 6%, well beyond 2028.
According to Chris Mouchabhani, Managing Partner at M Capital Group, “Despite all economic scenarios that one may consider, beyond overall economic shocks, medical technology should remain one of the most promising and robust sectors over the short to medium term and well beyond 2028.”
There is a movement towards home-based care for the elderly, next generation scanning and MRI devices, wearable technology, artificial intelligence incorporation, and online connectivity. Experts also see a focus on predictive, preventive, personalized, participatory, and precision medicine, with rising levels of integration of home care and technological innovation.
The average cost of treatment has been rising across the board, creating additional financial burdens to governments, healthcare providers and insurance companies. According to MCG, cost-per-inpatient-stay in the United States alone rose on average annually by over 13% between 2014 to 2021, leading MedTech to focus research efforts on optimized medical equipment at lower price points, whilst emphasizing portability and ease of use. Namely, 46% of the 1,008 medical technology companies in the 2021 MedTech Innovator (“MTI”) database are focusing on prevention, wellness, detection, or diagnosis, signaling a clear push for preventive care to also tackle costs.
In addition, there has also been a lasting impact on consumer and medical demand for home care, supported by the pandemic. Lockdowns, closure of care facilities, and healthcare systems subjected to capacity pressure, accelerated demand away from traditional inpatient care. Now, outpatient care solutions are driving industry production, with nearly 70% of recent diagnostics start-up companies producing products in areas such as ambulatory clinics, at-home care, and self-administered diagnostics.
The Importance of Community Nursing Care.pdfAD Healthcare
NDIS and Community 24/7 Nursing Care is a specific type of support that may be provided under the NDIS for individuals with complex medical needs who require ongoing nursing care in a community setting, such as their home or a supported accommodation facility.
How many patients does case series should have In comparison to case reports.pdfpubrica101
Pubrica’s team of researchers and writers create scientific and medical research articles, which may be important resources for authors and practitioners. Pubrica medical writers assist you in creating and revising the introduction by alerting the reader to gaps in the chosen study subject. Our professionals understand the order in which the hypothesis topic is followed by the broad subject, the issue, and the backdrop.
https://pubrica.com/academy/case-study-or-series/how-many-patients-does-case-series-should-have-in-comparison-to-case-reports/
How many patients does case series should have In comparison to case reports.pdf
A Standards-based Approach to Development of Clinical Registries - Initial Lessons Learnt from the Gestational Diabetes Registry
1. A Standards-based Approach to Development
of Clinical Registries -
Initial Lessons Learnt from the Gestational Diabetes Registry
Dr. Koray Atalag MD, PhD, FACHI (National Institute for Health Innovation)
Aleksandar Zivaljevic, PhD candidate (Univ. Of Auckland)
Dr. Carl Eagleton MBChB, FRACP (Counties Manukau District Health Board)
Karen Pickering (Diabetes Projects Trust)
2. Registry defined
An organised system that
uses observational study methods
to collect uniform data(clinical and other)
to evaluate specified outcomes for a population
defined by a particular disease, condition, or exposure,
and that serves a predetermined scientific, clinical or
policy purpose(s).
GliklichR, Dreyer Ne. Registries for Evaluating Patient Outcomes: A User's Guide Prepared by Outcome DEcIDECenter[Outcome Science, Inc.
dbaOutcome] under Contract No. HHSA290200500351TO1). Rockville, MD: Agency for Healthcare Research and Quality, 2007; Publication No.
07-EHC001-
3. Clinical Registries
Register / Registry
Clinical (+quality) / disease / patient / incidence / screening etc.
Repository of individuals with certain conditions/characteristics
Ease of access to important info
Track clinical processes & (risk adjusted) outcomes
Longitudinal history of correspondences & interventions
Prompt / feedback to participants and providers
Data linkages & advanced analytics & reporting
Supporting clinical practice
◦ Screening, risk prediction, intervention/recall, safety monitoring
Clinical quality improvement
◦ Organisations, clinicians, policy makers
Research & education
4. Why do we need them?
Because we don’t have the mighty EHR!
Registries are a ‘quick fix’ to some ‘can’t wait’ type
problems / for ‘quick wins’; capturing
◦ observations, diagnoses, procedures, clinical processes and
most importantly outcomes
Provide an infrastructure on which intervention studies
can be established with relative ease.
Who get’s a registry?
◦ Those with funding of course!
Clinical significance / popularity (eg. CVD, diabetes)
Well established network/specialised (e.g. Spina Bifida)
national/intl policies (MoH / WHO – cancer etc.)
leadership / persistence / charisma / luck (GDM?)
5. Around the world & NZ
A lot of them!
Overarching principles / regulations /
minimal standards
Shared resources (hosted by dedicated
organisations / infrastructure)
A growing number of them
All go own ways – (under privacy rules)
Hosted/curated by source groups with limited
technical/data management resources
Some hosted offshore (e.g. Oz)
6. GDM Registry
* A recently deployed pilot project to test the
feasibility of a registry to support targeted
interventions. Led and supported by CMH & DPT.
NIHI has undertaken health informatics research
and the technical development.
AIMS:
100% successful screening of women for type 2 diabetes
(T2DM) within 3 months after a pregnancy with GDM
Annual screening of all women for new onset T2DM
Early warning to healthcare providers (GPs, Maori/Pacific
Health, others) about GDM history in subsequent
pregnancies
7. Motivation for the GDM Registry
Long term consequences can be prevented by regular
screening for early detection of T2DM or high CVD risk
◦ CMDHB found 20% of women with a history of GDM were not
follow-up tested in a 4 year period; (37% for 2 year period)
◦ Sending out reminders improve adherence / better compliance
with screening recommendations
Risk of developing T2DM can be substantially reduced
by early identification of women at high risk + targeted
lifestyle & pharmacological interventions
Registry can also be used to drive clinical quality
improvement and enhance patient safety
◦ by identifying variations in processes and clinical outcomes.
8. GDM Registry Pathway
Entry
• Referral from primary care with a diagnosis of GDM
Education
• Attendance at Group Session
• Registry information supplied
Consent
• Attendance at DiP Clinic
• Consent obtained and entry into the registry
Postpartum
• 6 week OGTT request or 3 month HbA1c
• GP & Patient advised of results
Annual
• Annual HbA1c with copy to primary care
• GP & Patient advised of results
Next time
• Positive pregnancy test detected in Testsafe
• Requesting healthcare provider advised of Diabetes history by the Registry
RegistryDirected
9. Golden principle: Minimal data entry, Maximal reuse!
Health Informatics @ Work
Used an international (and HISO) standard:
◦ Consistent dataset
◦ Interoperability / integration
◦ Manage change over time
Used a Web-based data set development tool to
review & finalise
Automatically converted dataset into “software
code” [domain objects]
Built on NIHI’s data management framework
10. If the Banks Can Do It,
Why Can’t Health?
Clinical data is wicked:
◦ Size (breadth, depth) and complexity
◦ >300,000 concepts, 1.4m relationships in SNOMED
◦ Variability of practice
◦ Diversity in concepts and language
◦ Conflicting evidence
◦ Longevity
◦ Links to others (e.g. family)
◦ Peculiarities in privacy and security
◦ Medico-legal issues
It IS critical…
11. Open source specs & software for representing
health information and person-centric records
◦ Based on 18+ years of international implementation experience
including Good European Health Record Project
◦ Superset of ISO/CEN 13606 EHR standard
◦ Underpins HISO Interop Reference Architecture standard (NZ)
Not-for-profit organisation - established in 2001
www.openEHR.org
Extensively used in research
Separation of clinical
and technical worlds
Big international community
19. EHR Providing a Canonical Representation
so we know what kind of info goes into which bucket!
Demographics
ClinicalEncounter
VitalSigns
Medications
Diagnoses
DiagnosticTests
Interventions
FamilyHistory
PastHistory
PhysicalExam
Genetics
LifeStyle
etc.etc.etc.
Subject A
Subject B
Person-Centric Record Organisation
NZ Address
Ethicity1,2.
Whanau
USAddress
State
Next of kin
GP visit
Flu-like
PHO enrolm.
Hospital adm.
Diabetes
Priv insurance
BP 130/90
HR 90
T: 38.5 C
BP 120/70
(24 hour avg)
HR 70
T: 37 C
Rx A
Dispense
Administer
Rx B
Dispense
Administer
Dx 1
Dx 2
etc.
Diabetes Dx
-Type
-Severity
-Course etc.
Routine Blood
Urine
X-Ray
Specific blood test
Urine culture
Genomic assay
Retinography
Rx
Fluid Tx
Insuline inj
Infection Tx
Psychologic
N/A
Pedigree
N/A
Chronic
Routine
Detailed
Foot and
eyes
N/A N/A
DNA
Seq.
Assays
Low
sugar
Exercise
Shared Archetypes
Each finding usually depends on other – clinical context matters!
20. Benefits of Approach Taken
We may not have EHR now....but
by using openEHR to represent our clinical information
we are leveraging some of the benefits of EHR today,
including
◦ Expressivity, clinical context, meta-data support
◦ Interoperability
◦ Semantic querying (easy + fast)
◦ Tooling support and international content
◦ Standards compliance
and future-proofing registry data!
Atalag K, Yang HY, Tempero E, Warren JR. Evaluation of software maintainability with
openEHR – a comparison of architectures. International Journal of Medical Informatics.
2014 Nov;83(11):849–59.
21. Conclusions
No need for Regional Ethics Approval if ‘part of
clinical service’
Model based Dataset development
◦ Very effective and easy to engage clinicians but require
tooling and editorial effort & skills
Fully-fledged EHR underpinning Registry
◦ Standards based, scientific rigour in data representation
Getting ‘information right’ is crucial!
◦ Invest in defining dataset properly, change is costly
◦ Alignment is hard and there’s no formal guidance There
is no single organisation or mechanism to ensure the
Sector’s datasets are to be aligned
22. What’s Next
Obtain funding for next stage
Further Enhancements
Prepare for scaling up & further testing of the Software
New data points & features (e.g. Smart phone App for women for bi-
directional support)
Integration with key systems (e.g. PAS, Maternity System)
Deployment in CMH catchment area
◦ Attain enough numbers to for meaningful formal evaluation
Seek wider Sector support & funding
◦ National Diabetes Registry?
NIHI has implemented other Registries (NZ Cardiac registry)
and providing stewardship to research databases (Growing
Up in NZ, SPARX + 100s of own trials). Current
infrastructure and expertise will be leveraged.
These capture observations, diagnoses, procedures, clinical processes and most importantly outcomes and for example may include patients treated with a particular drug, device or surgical procedure (e.g. joint replacement), with a particular illness (diabetes), and utilising a specific healthcare resource (e.g. treated in ICU).
Better utilisation of health information is a necessity for delivering on the pressing requirements of modern clinical practice to provide the best available medical care for individuals, yet equitable and sustainable for the society over time. While the ultimate aim is to have the longitudinal and lifelong electronic health record that is accessible whenever and wherever needed, because it doesn’t exist, clinical registries are established to collect information about individuals in areas where improvement in practice is of high importance. These capture observations, diagnoses, procedures, clinical processes and most importantly outcomes and for example may include patients treated with a particular drug, device or surgical procedure (e.g. joint replacement), with a particular illness (diabetes), and utilising a specific healthcare resource (e.g. treated in ICU).