The document summarizes the development of an electronic nursing record (ENR) in a hospital in the Netherlands. It discusses how the hospital is working with nurses and a vendor to develop a model-based ENR using standardized nursing terminology and clinical models. Over 50,000 paper nursing records were analyzed to identify 315 unique forms and 5,500 specific patient records. Working groups of nurses then developed detailed clinical models with standardized terms and variables to organize this information for the ENR. The standardized models are now being programmed into the database structure of the new ENR system. The approach has been successful in making nursing the leading group and stimulating nursing knowledge.
Healthcare institutions are aggressively moving towards meeting compliance with MU1 and MU2 with the implementation of full-featured Electronic Health Records. Concomitantly, there will be a massive increase in the amount of clinical data captured electronically. Business intelligence (BI) which traditionally has focused on financial data can be leveraged to use clinical data to support providers in delivering high quality, efficient care. In addition, BI coupled with population health analytics can help meet many Accountable Care Organization needs. This presentation will discuss the Denver Health journey in using BI in a variety of was to facilitate the attainment of high quality care.
"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.
Usability-focused Clinical Decision Support with the Help of Semantic Technologies. Braga S. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
Healthcare institutions are aggressively moving towards meeting compliance with MU1 and MU2 with the implementation of full-featured Electronic Health Records. Concomitantly, there will be a massive increase in the amount of clinical data captured electronically. Business intelligence (BI) which traditionally has focused on financial data can be leveraged to use clinical data to support providers in delivering high quality, efficient care. In addition, BI coupled with population health analytics can help meet many Accountable Care Organization needs. This presentation will discuss the Denver Health journey in using BI in a variety of was to facilitate the attainment of high quality care.
"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.
Usability-focused Clinical Decision Support with the Help of Semantic Technologies. Braga S. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
Designing and launching the Clinical Reference LibraryKerstin Forsberg
Presentation for the European Clinical Data Forum conference, 24 May, 2011. Describing the business problems and drivers behind the design of a ISO11179 based metadata registry for clinical data. And also introducing the features of the CRL application.
Automated and Explainable Deep Learning for Clinical Language Understanding a...Databricks
Unstructured free-text medical notes are the only source for many critical facts in healthcare. As a result, accurate natural language processing is a critical component of many healthcare AI applications like clinical decision support, clinical pathway recommendation, cohort selection, patient risk or abnormality detection.
Semantic Web Technologies: A Paradigm for Medical InformaticsChimezie Ogbuji
Some common needs for the patient registries, Electronic Health Record (EHR) systems, and clinical research repositories of the future are: semantic interoperability, adoption of standardized clinical terminology, adhoc and distributed querying interfaces, and integration with extant databases and web-based systems. A suite of standards has recently emerged from the consortium responsible for the development and oversight of the protocols of the World-wide Web (WWW). They were conceived to address data integration challenges associated with internet and intranet applications. Many of these standards and technologies are capable of addressing the challenges common to health information systems. In this talk, an introductory overview of these technologies, how they address these challenges, and a brief discussion of projects where they have been used is given.
An overview of clinical healthcare data analytics from the perspective of an interventional cardiology registry. This was initially presented as part of a workshop at the University of Illinois College of Computer Science on April 20, 2017.
On the extended clinical workflows for personalized healthcareMilan Zdravković
Zdravković, M., Trajanović, M., On the extended clinical workflows for personalized healthcare, International IFIP Working Conference On Enterprise Interoperability (IWEI 2013), March 27th - 28th, 2013, Enschede, The Netherlands. In: M. van Sinderen et al. (Eds.): IWEI 2013, LNBIP 144, pp.65-76, 2013
HospitalSoftwareShop Eye Clinic is easy-to-use software for Ophthalmologists to automate their eye clinics. The USP of HSS software for eye clinics is direct machine integration of AR, Keratometer, AutoLensometer, Tonoscope, thereby removing manual inervention. That's why ophthalmologists across the globe prefer HSS Software for eye clinics
RDAP 16 Poster: Measuring adoption of Electronic Lab Notebooks and their impa...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Poster session (Wednesday, May 4)
Presenters:
Jan Cheetham, University of Wisconsin-Madison
Wendy Kozlowski, Cornell University
The Logical Model Designer - Binding Information Models to TerminologySnow Owl
This presentation demonstrates the functionality provided by the Logical Model Designer (LMD) and Snow Owl tools, which enables terminology to be bound to the Singapore Logical Information Model.
Abstract:
A critical enabler in the journey towards semantic interoperability in Singapore is the Singapore "˜Logical Information Model' (LIM). The LIM is a model of the healthcare information shared within Singapore, and is defined as a set of reusable "˜archetypes' for each clinical concept (e.g. Problem/Diagnosis, Pharmacy Order). These archetypes are then constrained and composed into "˜templates' to support specific use cases.
The Singapore LIM harmonises the semantics of the information structures with the terminology, using multiple types of terminology bindings, including semantic, value domain and constraint bindings. Value domain bindings are defined to both national "˜reference terminology' (used for querying nationally-collated data), as well as to a variety of "˜interface terminologies' used within local clinical systems (required to enforce conformance-compliance rules over message specifications generated from the LIM). To support the diversity of pre-coordination captured in local interface terms, "˜design patterns' are included in the LIM, based on the SNOMED CT concept model. These design patterns represent a logical model of meaning for a specific concept, and allow more than one split between the information model and the terminology model to be represented in a semantically-consistent manner.
This presentation will demonstrate the "˜Logical Model Designer' (LMD) - an Eclipse-based tool that is being used to maintain Singapore's Logical Information Model. A number of features of the LMD tooling will be demonstrated, with a specific focus on how the information structure is bound to the terminology via an interface to the Snow Owl platform. Value Domains are defined as reference sets within Snow Owl and then linked to the information structures defined in the LMD.
Please see our website http://b2i.sg for further information.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Designing and launching the Clinical Reference LibraryKerstin Forsberg
Presentation for the European Clinical Data Forum conference, 24 May, 2011. Describing the business problems and drivers behind the design of a ISO11179 based metadata registry for clinical data. And also introducing the features of the CRL application.
Automated and Explainable Deep Learning for Clinical Language Understanding a...Databricks
Unstructured free-text medical notes are the only source for many critical facts in healthcare. As a result, accurate natural language processing is a critical component of many healthcare AI applications like clinical decision support, clinical pathway recommendation, cohort selection, patient risk or abnormality detection.
Semantic Web Technologies: A Paradigm for Medical InformaticsChimezie Ogbuji
Some common needs for the patient registries, Electronic Health Record (EHR) systems, and clinical research repositories of the future are: semantic interoperability, adoption of standardized clinical terminology, adhoc and distributed querying interfaces, and integration with extant databases and web-based systems. A suite of standards has recently emerged from the consortium responsible for the development and oversight of the protocols of the World-wide Web (WWW). They were conceived to address data integration challenges associated with internet and intranet applications. Many of these standards and technologies are capable of addressing the challenges common to health information systems. In this talk, an introductory overview of these technologies, how they address these challenges, and a brief discussion of projects where they have been used is given.
An overview of clinical healthcare data analytics from the perspective of an interventional cardiology registry. This was initially presented as part of a workshop at the University of Illinois College of Computer Science on April 20, 2017.
On the extended clinical workflows for personalized healthcareMilan Zdravković
Zdravković, M., Trajanović, M., On the extended clinical workflows for personalized healthcare, International IFIP Working Conference On Enterprise Interoperability (IWEI 2013), March 27th - 28th, 2013, Enschede, The Netherlands. In: M. van Sinderen et al. (Eds.): IWEI 2013, LNBIP 144, pp.65-76, 2013
HospitalSoftwareShop Eye Clinic is easy-to-use software for Ophthalmologists to automate their eye clinics. The USP of HSS software for eye clinics is direct machine integration of AR, Keratometer, AutoLensometer, Tonoscope, thereby removing manual inervention. That's why ophthalmologists across the globe prefer HSS Software for eye clinics
RDAP 16 Poster: Measuring adoption of Electronic Lab Notebooks and their impa...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Poster session (Wednesday, May 4)
Presenters:
Jan Cheetham, University of Wisconsin-Madison
Wendy Kozlowski, Cornell University
The Logical Model Designer - Binding Information Models to TerminologySnow Owl
This presentation demonstrates the functionality provided by the Logical Model Designer (LMD) and Snow Owl tools, which enables terminology to be bound to the Singapore Logical Information Model.
Abstract:
A critical enabler in the journey towards semantic interoperability in Singapore is the Singapore "˜Logical Information Model' (LIM). The LIM is a model of the healthcare information shared within Singapore, and is defined as a set of reusable "˜archetypes' for each clinical concept (e.g. Problem/Diagnosis, Pharmacy Order). These archetypes are then constrained and composed into "˜templates' to support specific use cases.
The Singapore LIM harmonises the semantics of the information structures with the terminology, using multiple types of terminology bindings, including semantic, value domain and constraint bindings. Value domain bindings are defined to both national "˜reference terminology' (used for querying nationally-collated data), as well as to a variety of "˜interface terminologies' used within local clinical systems (required to enforce conformance-compliance rules over message specifications generated from the LIM). To support the diversity of pre-coordination captured in local interface terms, "˜design patterns' are included in the LIM, based on the SNOMED CT concept model. These design patterns represent a logical model of meaning for a specific concept, and allow more than one split between the information model and the terminology model to be represented in a semantically-consistent manner.
This presentation will demonstrate the "˜Logical Model Designer' (LMD) - an Eclipse-based tool that is being used to maintain Singapore's Logical Information Model. A number of features of the LMD tooling will be demonstrated, with a specific focus on how the information structure is bound to the terminology via an interface to the Snow Owl platform. Value Domains are defined as reference sets within Snow Owl and then linked to the information structures defined in the LMD.
Please see our website http://b2i.sg for further information.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
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ABDOMINAL TRAUMA in pediatrics part one.drhasanrajab
Abdominal trauma in pediatrics refers to injuries or damage to the abdominal organs in children. It can occur due to various causes such as falls, motor vehicle accidents, sports-related injuries, and physical abuse. Children are more vulnerable to abdominal trauma due to their unique anatomical and physiological characteristics. Signs and symptoms include abdominal pain, tenderness, distension, vomiting, and signs of shock. Diagnosis involves physical examination, imaging studies, and laboratory tests. Management depends on the severity and may involve conservative treatment or surgical intervention. Prevention is crucial in reducing the incidence of abdominal trauma in children.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
1. ENR development in Netherlands
Working towards a model based
electronic nursing record
in practice
Niels Jansen, RN, BSN
Tino Bekkering, RN, BSN
OLVG Hospital, Amsterdam, The Netherlands
Dr. William Goossen, Results4Care, The Netherlands
2012-06-26
2. Introduction
o Who are we?
o Why are we here?
o Nurse based approach
o Modeling in nursing practice
3. Setting EHR in Dutch Hospitals
o EHR in the Netherlands is booming business
o From paper records to digital patient health records
o Few vendors in the Dutch market offer a fully integrated
EHR with standardizations
o Focus in hospitals based on technique rather than:
- standardization,
- process management
- content based with professional knowledge
4. Focus EHR
Patient safety
Patient involvement
Decision making
6. Project ENR in the OLVG
o Focus long term 2011 2020
o Nursing IT organization with nursing professionals with a degree in health
informatics
o Development of nursing standardization with multi classifications for a solid EHR
in the hospital
o Short term 2010 - 2015
o Close relationship between Nursing (professionals) and vendor (technique)
o Building knowledge of standardization and IT in the nursing organization
o Short term ENR solution, bringing standardization of generic forms and patient
data
7. The start of the project
2010
Collection nursing data (paper forms)
50.000 entries in excel spreadsheet (6 m)
315 unique forms with patient data 5500 records with specific patient data
Patient Name 169 entries Patient ID 33 entries
Quality indicators 5 forms per patient Fluid Balance 23 forms
Allergies 57 entries Vital Signs 41 forms
8. Diverse terminology Riva Rocci
Tension
Blood
pressure
What is my
under
pressure???
9. Method of standardization
• Current standards: ‘pretty solid but hard to move around’
• Focus Professional & Sustainable
10. Detail Clinical Models (DCM)
Organize
workinggroups
Determine
subjects
Review
Literature
EBP Determine
EBN all variables/
context Mapping
Coding
Working with Design
Excel spreadsheets
Word templates
Program
Test
Implement
Review
11. DCM table of content
Revision history Care Process
Concept Example of instrument
Mind map Issues
Purpose References
Evidence base Functional model
Information model (all the variables) Traceability to other standards
Example instances Disclaimer
Instructions Terms of use
Interpretation Copyrights
12. Realization
Realization of the standardization 2010-2011
Breathing Weight Pupil reaction
Nursing assessment (adm.) Heart frequency Oxygen Saturation
Blood pressure Length Tube feeding/products
Pressure sore risk Long excretion Temperature
measurement
Pressure sore wound care Stomach excretion Fluids in
Pressure sore wound Urination Fluids out
classification
Defecation Patient admin data Fluid balance
Delirium Person data Care Professional data
Glasgow Coma Scale Pain score Oxygen admin
13. DCM in ENR
Steps of processing DCM:
• Worddoc
• Excelsheet encoding
• Functional Design
• Development EVD
• Database configuration
15. Success
o The nursing profession is the leading group
o Stimulation of nursing knowledge and research
o Spreading the knowledge within the hospital
o Medical profession Vital Records – Fluid balance
o Close relationship between vendor and nurses
17. Questions?
Niels Jansen, OLVG
William Goossen, Results4Care
Editor's Notes
Welcome to this presentation where I will explain how the nursing profession, in a dutch hospital, in the centre of Amsterdam has implemented a model based electronic nursing record. Why is this so unique? Standardization and modeling of patient data is well known around the world, but the challenge was how to bring these with the use of an Electronic Health Record into the daily practices. My name is Niels Jansen. I am projectmanager of the project “Electronic Nursing Record and clinical pathways”. My background,… critical care nurse/ paramedic/ Nurse manager pulmonary unit I am also vice chairman of the nursing staff within the OLVG, and in 2009 I initiated this project and in collaboration with the Board of Directors, I formulated the mission of the project.
The nursing record as part of the larger electronic health record has been discussed for years. Its implementation is not as widespread as often considered. In the Netherlands for example, a fraction of hospitals use it. This presentation describes a nurse led project in a Dutch hospital where an electronic nursing record has been defined. This is based on an requirements analysis and standardization through Detail Clinical Models (DCM). The OLVG is the first hospital in the Netherlands using the DCM method for determining nursing data. In addition, where other HR projects often only use one terminological system, this project uses multiple terminologies. This project was carried out together with Tino Bekkering, an informatics nurse, the OLVG Hospital and William Goossen, an international expert in developing nursing standards. In collaboration with five nurses with a degree in Health Informatics and 25 nurses from the hospital wards, we standardized the patient data with DCM’s, a method and format to organize clinical knowledge, concepts, and data elements. The answer to the question; “How do you transform the theory of standardization & modeling in the daily nursing practice and how is it able to contribute to overall quality of care?” will be further explained in this presentation.
Registration of patient data drivers with different IT applications and paper records. Little or no standardization based on classifications is applied or integrated in EHR systems
The focus of EHR development is a growing issue in every country.
Our hospital, is a known top clinical hospital in Amsterdam, wanted to achieve the goal of introducing an Electronic Nursing Record (ENR). Now we will show you short film of our hospital to get an idea of the setting where we carried out and still carry out this project.
The project includes a short term ENR solution with limited functions, using standardized generic forms, and a proper nursing record specification for a future long term solution, to be integrated in a multidisciplinary Electronic Health Record (EHR). From the outset, the goal was to have a short term ENR and a long term EHR with the specifications useful in different areas. For this, the standardization approach with DCM was chosen and approved by the hospital board. At the same time we recruited nurses to get a degree in Health Informatics with the goal of building up a Nursing IT organization.
In 2010, as part of the ENR project, the nurses carried out a complete inventory of all the available forms and documentation methods on 15 wards. All the data was entered into an Excel spreadsheet, sorted, compared, and analyzed accordingly. This took us about six months at the start of the project. The results are shown here. Other steps were: A literature study of ENR specifications, implementations, standardization and modeling to formulate a mission and strategy how to design and implement an ENR Interviews have been held with nurses from the wards, to get to know how nurses use the forms in their daily practice Finally we visited some hospital sites in the Netherlands and in the US to exchange experiences and knowledge of ENR development
Based on the results of the information analysis, the conclusion was, that there was a large diversity of forms, data entry methods and places and duplication of data. Different forms often included the same data. There was no accepted terminology or data representation and there was a defining of information in different ways. Forms for the same clinical purpose had different names. See, for example, the concept of blood pressure
Generic forms, such as patient identification, vital signs, fluid balance, and quality indicators such as pain scales, pressure ulcer instruments, and SNAQ were created based on the DCM standardized data, mapped with SNOMED CT, LOINC and others. DCM is both a method and a format to organize clinical knowledge, represent concepts, and define data elements in such a manner that it allows managing and exchanging information without the need to use a specific technology. So why not use nursing terminology systems? Our opinion is that they don’t always have the precise code for every concept. We are looking for the best fit! Explain process with value blood pressure
The ENR project started with a group of nurses and informatics nurses carrying out the following steps: Sorting out the data in the Excel spreadsheet in logical groupings Next, the data was mapped into consistent categories of patient data. Applying the DCM methodology in which the evidence base for concepts was explored and summarized. The data elements where specified, and meta-information on authorship, version control and publication dates were added. Where required, the informatics nurses added the meta-information and technical descriptions as well.
The OLVG templates are a variation of the national guidelines for DCM in order to allow the practicing nurses to complete the relevant section. Then the project team encoded the data with technical specifications of the data elements. Everywhere, all the required DCM parts are included.
The ENR project group realized during the biweekly meetings of 2 hours that within a period of 6 months, a total of 28 DCM’s was developed, of which 9 where localized from within the national repository, and 19 where developed either from scratch, or completed using existing unfinished draft materials.
Example blood pressure The DCM Word template allowed the nurses to complete the content. They used the Excel spreadsheet to list the data elements. Next, the informatics nurses and external expert completed the technical specification, unique coding, and meta-information. DCM are then placed in the hospitals document management system, allowing governance in the hospitals quality system. The final step is the actual functional design and the system development and implementation. In some instances, the creation of functional design and/or implementation generated new questions. example: measuring the blood pressure at the left or right ankle
There were some lessons to be learned! It took quite some time for the nurses to start with the creation of DCM and the methodology behind it. Several sessions where needed for education and encouragement. Searching for all the background knowledge remains a time consuming effort. Splitting the DCM into two templates, a Word template for the nursing content and an Excel template for the data element specification, allowed the nurses to concentrate on the professional content. Later, the informatics nurses and an external expert completed the technical parts and coding of the data elements and value sets. The bottom up approach to DCM standardization increased the support and acceptance among nurses. The ENR is recognized as a professional content based system, and not something imposed by some vendor. The quality indicators that are standardized via DCM support the registration of data at the point of care, which has a huge efficiency generating approach. The criteria to choose some data elements from the DCM among others are relevance, time and budget. The project gained external expertise for the standardization of nursing documentation. Another positive effect is that it helps in the discussions with the vendor: requirements are very precise and well specified, making the programming task easier.
Since the beginning of 2012 we started to implement the ENR with COWS. At the moment 6 wards, around 300 nurses and doctors are using the ENR in the daily practice. At the end of 2012 all 15 wards will be using the ENR. Interview in 30 seconds with nurse behind a COW