The document discusses future trends in decision support systems (DSS). It predicts that within the next 10-15 years, DSS tools will be able to pull data from more sources and integrate data more effectively through increased classification. DSS interfaces will also likely shift to tablets and require less training to use. The role of artificial intelligence in DSS is explored along with potential applications like virtual reality and telemedicine. The document concludes by emphasizing the importance of health informatics and cautions that solutions must be appropriate and not cause undue clinical error.
Organization’s success depends on quality of managers’ decisions
When decisions involve large amounts of data and complex processing, a DSS is a valuable tool
When decision making involves many uncertainties and/or lots of alternatives a DSS is needed
Training Slides of Decision Support System, discussing how the system as an interactive computer-based system that is being effectively used in communications technologies.
Some keypoints:
- The Decision Support Paradigm
- Basic Concepts of DSS
- Examples of DSS
For further information regarding the course, please contact:
info@asia-masters.com
Organization’s success depends on quality of managers’ decisions
When decisions involve large amounts of data and complex processing, a DSS is a valuable tool
When decision making involves many uncertainties and/or lots of alternatives a DSS is needed
Training Slides of Decision Support System, discussing how the system as an interactive computer-based system that is being effectively used in communications technologies.
Some keypoints:
- The Decision Support Paradigm
- Basic Concepts of DSS
- Examples of DSS
For further information regarding the course, please contact:
info@asia-masters.com
Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
Knowledge management and information systemnihad341
this file would help you in writing your assignment on knowledge management and information system. I did this for a student of UK. He got a very satisfactory marks from it. Then i thought that why not help others. The course is a complex one. So, this would be my pleasure if someone really found this useful.
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El campo de DSS / BI esta evolucionando desde sus origenes como una herramienta primariamente de soporte personal y está rapidamente llegando a ser una comodidad compartida a traves de de las organizaciones
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
How to Interpret and Plan for the 2014 CMS CEHRT Rule Iatric Systems
* Flexibility Plan 2014 and what we know
* Mickey Waters, IT Director at Conway Medical Center – Why he chose to take advantage of the rule
* Lyndel Mead, RN, MSN, Clinical Informatics Coordinator at Peterson Regional Medical Center – Why he chose not to take advantage of the rule
* Making the best decision for your organization
* How to get personalized, expert MU advice
The third webcast in this series focuses on ways to meet your health system’s specific needs and achieve a 360-degree view of your patients, processes, physicians, and costs without purchasing multiple, disparate solutions, and creating information silos.
Our speakers discuss their collective experience in working with organizations to create tailored platforms that provide convenient access to data collected by, and stored in, disparate clinical information systems and enabling that data to be securely used by users throughout the broader healthcare community. Actionable data – available to all users when they need it – serves as a foundation for analysis and decision-making aimed at improving how care is delivered.
You can find it online at http://www.informationbuilders.com/webevents/online/24637#sthash.RnwoH27x.dpuf
Webinar: Leveraging big data in life sciences & healthcareKnowledgent
Slides from May 2014 webinar hosted by Knowledgent, Hortonworks, and the CEOi. Titled “Leveraging Big Data in the Life Sciences and Healthcare,” the webinar featured thoughts on using big data to further Alzheimer's care delivery from Justin Sears, Industry Specialist at Hortonworks, Drew Holzapfel, Executive Director from the Global CEO Initiative on Alzheimer’s Disease, and Knowledgent's Tom Johnstone and Chris Young.
A look at benefits realisation during every phase of transformation activities to operationalise portable digital health records
Day Two, Pop-up University 2, 09.00
Unleash Enterprise Innovation with Sogeti’s Industry SolutionsCapgemini
Sogeti’s industry solutions, built on Hewlett Packard Enterprise ConvergedSystem for Microsoft Analytic Platform System (APS) and Power BI, create a proven platform for visualizing, modeling and reporting data insights for industries including Healthcare and Retail.
Learn how to unite structured inpatient and outpatient data. Find out how to converge real-time inventory visualization and notifications from external sources and Social Media. Learn to capture and analyze data to improve your decision-making.
Presented at Discover London 2015.
Discover How Allscripts Uses InfluxDB to Monitor its Healthcare IT PlatformInfluxData
Discover How Allscripts Uses InfluxDB to Monitor its Healthcare IT Platform
Allscripts is an industry leader in electronic health record (EHR) system integration and healthcare information technology. Its platform is used to help healthcare organizations drive better patient care, improve financial and operational outcomes and advance clinical results. Its solution connects healthcare professionals with data across the open platform. Allscripts uses a time series database to become data-driven by gaining observability into its platform to help healthcare organizations maximize application availability.
Join this webinar to learn about:
Allscripts effect on healthcare delivery
Its DevOps approach that has improved service uptime
How InfluxDB enables better data correlation and reporting
Collaborative visualization supporting complex data driven service build - Bi...webwinkelvakdag
Most service development methods I encountered where 'point solutions' focusing on details. Rarely I saw methods that supported me to get a consistent umbrella overview of the whole service operation. I searched for such a tool during the nearly 20 years of managing field-service and professional-service organizations. A tool that is some sort of collaborative method (or visual model) that assists multidisciplinary teams to build innovative data driven services. In the presentation I share how the current visual model like checklist (in Dutch called a ‘praatplaat’) came about and how is works. Furthermore I discus future developments.
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
Building an Intelligent Biobank to Power Research Decision-MakingDenodo
This presentation belongs to the workshop: "Building an Intelligent Biobank to Power Research Decision-Making", from ISBER 2015 Annual Meeting by Lori A. Ball (Chief Operating Officer, President of Integrated Client Solutions at BioStorage Technologies, Inc), Brian Brunner (Senior Manager, Clinical Practice at LabAnswer) and Suresh Chandrasekaran (Senior Vice President at Denodo).
The workshop cover three different topic areas:
- Research sample intelligence: the growing need for Global Data Integration (Biobank Sample and Data Stakeholders).
- Building a research data integration plan and cloud sourcing strategy (data integration).
- How data virtualization works and the value it delivers (a data virtualization introduction, solution portfolio and current customers in Life Sciences industry).
The biomedical R&D environment is increasingly dependent on data meta-analysis and bioinformatics to support research advancements. The integration of biorepository sample inventory data with biomarker and clinical research information has become a priority to R&D organizations. Therefore, a flexible IT system for managing sample collections, integrating sample data with clinical data and providing a data virtualization platform will enable the advancement of research studies. This workshop provides an overview of how sample data integration, virtualization and analytics can lead to more streamlined and unified sample intelligence to support global biobanking for future research.
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
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
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
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
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
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
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2. Learning Objectives
• Explain Versions of DSS
• Explore Expectations from DSS
• Understand Classification of data
• Recognize Training
• Explain Interface of tools
• Understand Group DSS
• Explore Decision Support Centre
• Explain Strategic DSS
• Understand Intelligent DSS
• Explain Future of DSS
27/04/2015 2Dr. Bashir/CPHHI/UH/2014
3. Versions of DSS
• Many Of The Popular DSS Tools That Was The Big Hit
Around Year 2000 Has Been Transformed Into Full Business
Intelligence Tools Where You Can Get A Broad Overview
Over All Your Live Data And Easily Make Those Tough
Decisions Based On The Facts And Data Instead Of
Guesses.
• The New Versions Of Decision Support Systems Makes It
Even Easier For You To Make The Right Choices, They
Prevent The Data In A Much Better Way Than Earlier, The
Data Is More Accurate And They Are Able To Pull The Data
From Additional Sources.
27/04/2015 3Dr. Bashir/CPHHI/UH/2014
4. Expectation from DSS
• But What Can We Expect In 10 Or 15 Years From
These DSS Tools? In The Next 1-2 Years We Will Be
Seeing A Large Increase In Apps For Tables Like The
Apple Ipad And Similar Devices.
• There Are Already A Few Of Those On The Market
But The Quality Isn’t That Good Yet And There Are
Still A Lot Of Room For Improvements.
• We Will See DSS Tools That Can Pull Data From An
Even Wider Range Of Sources As More And More
Data Gets Stored And Filed In The Systems.
27/04/2015 4Dr. Bashir/CPHHI/UH/2014
5. Classification of Data
• We Integrate The Various Sources More,
There Is Much More Classification Of Data
Too. This Will Help The Decision Support
System To Make An Even Better Overview
And Help You Make Some More Accurate
Decisions And Making These Types Of
Business Intelligence Software Systems
Even More Valuable Than Today.
27/04/2015 5Dr. Bashir/CPHHI/UH/2014
6. Training
• Right Now Many Of The Systems Require A Lot
Of Training To Use Properly, That Part Is Also
Something We Can Expect To See Improved In
The Near Future So That Even People Will
Almost No Training Will Be Able To Get The
Right Data From The Various Systems And Be
Able To Use Types Kind Of DSS Programs
Easily.
27/04/2015 6Dr. Bashir/CPHHI/UH/2014
7. Interface Of Tools
• The Interface Is Like To Change Quite A Bit And
Most Of The Tools Will Very Likely Change To Be
Used Mainly On Tablets Instead Of Regular
Computers.
• This Also Means That They Will Need To Either
Pre Process The Data On A More Powerful
Computer First Or Make Optimizations To The
DSS Tools So That They Can Much Easier Process
The Data Without Having To Use Some Heavy
CPU And Ram Power In Order To Do So.
27/04/2015 7Dr. Bashir/CPHHI/UH/2014
8. Group DSS
• Group Decision Making Plays A Major Role
In Determining Corporate Affairs .
• How To Design The Group DSSs For
Supporting Group Meetings Is A Complex
Task Because Of The
– Complex Combination Of People, Places
– Time , Communication Networks
–Individual Preferences, And Other Technologies.
27/04/2015 8Dr. Bashir/CPHHI/UH/2014
9. • A Group Meeting Can Be Conducted At The
Same Place, Or At Different Places Attended By
Different Groups Of People Using
Teleconferencing Techniques.
• On The Other Hand, A Group Meeting Can Be
Conducted During A Fixed
Period Of Time, Or It Is Just An Unlimited On-
going Process.
• Group DSS Is Supposed To Support Any One Of
The Possible Combinations.
Group DSS
27/04/2015 9Dr. Bashir/CPHHI/UH/2014
10. Decision Support Centre
• Decision Support Centre Is An Emerging
Concept.
• A Decision Support Group Is Staffed By
Information Systems Professionals Who
Understand The Business
Environment, Form The Core Of Decision
Support Centre, With Advanced
Information Technology.
27/04/2015 10Dr. Bashir/CPHHI/UH/2014
11. • A Decision Support Centre Is Usually
Located In Close Proximity To Top
Management So That Instant Decision
Support Can Be Provided.
• A Decision Support Group Will Readily
Develop Or Modify DSSs To Support Top
Management In Making
Urgent And Important Decisions.
Decision Support Centre
27/04/2015 11Dr. Bashir/CPHHI/UH/2014
12. Strategic DSS
•DSS For Supporting Strategic
Management Is A Well Recognized Area
Of Importance And Significance .
•It Is An Area Where DSS Can Make A
Substantial
Impact On The Top Management And
Corporation.
27/04/2015 12Dr. Bashir/CPHHI/UH/2014
13. Intelligent DSS
•Some Authors, Notably Nolan (1986),Suggested
The Adaptation Of Artificial Intelligence (AI)
and Expert Systems Techniques To DSS.
•However, Most Authors Under-estimate The
Difficulties In Representing Commonsense
Knowledge Which Is An Unsolved Problem In
AI.
27/04/2015 13Dr. Bashir/CPHHI/UH/2014
14. The DSS of the Future
• Intelligent DSS Should Be More Practical.
• Future DSS Should Be Creative.
• Latest Advances In Computer Technology
Improving DSS.
27/04/2015 14Dr. Bashir/CPHHI/UH/2014
15. • Larger Role For
– Management Science
–Cognitive Psychology
–Behavioral Theory
–Information Economics
–Computer Science
–And Political Science
The DSS Of The Future
27/04/2015 15Dr. Bashir/CPHHI/UH/2014
16. • Improved DSS Apply To More
Unstructured Problems.
• Must Be Able To Create Alternatives
Independently.
• Much Longer-range Perspective Of DSS
Research.
The DSS Of The Future
27/04/2015 16Dr. Bashir/CPHHI/UH/2014
17. The DSS of the Future
• Research On Interactions Between Individuals
And Groups.
• More Examination Of The Human Component
Of DSS: Learning And Empowerment.
• Enhancement Of DSS Applications With Values
And Ethics.
The DSS Of The Future
27/04/2015 17Dr. Bashir/CPHHI/UH/2014
18. The DSS of the Future
• Major Research In Human-machine
Interfaces And Their Impacts On
Creativity And Learning.
• Organizational Impacts Of DSS.
• Decision Support System Products Are
Incorporating Artificial Intelligence:
Intelligent DSS.
The DSS Of The Future
27/04/2015 18Dr. Bashir/CPHHI/UH/2014
19. The DSS of the Future
• Focused Versions Of DSS Toward Specific Sets
Of Users Or Applications.
• Continued Development Of User-friendly
Capabilities.
• The DSS Software Market Continues To
Develop And Mature.
The DSS Of The Future
27/04/2015 19Dr. Bashir/CPHHI/UH/2014
20. 20
Virtual Reality
• Virtual Reality System: Enables One Or More
Users To Move And React In A Computer-
simulated Environment
• Immersive Virtual Reality: User Becomes Fully
Immersed In An Artificial, Three-dimensional
World That Is Completely Generated By A
Computer
27/04/2015 Dr. Bashir/CPHHI/UH/2014
21. 21
Interface Devices
• Head-Mounted Display (HMD)
• CAVE (Computer Assisted Virtual Environment )
– Projects Stereo Images On Walls And Floor Of A
Room-sized Cube
• Earphones
• Haptic (Touch) Interface
– Relays Sense Of Touch And Other Sensations In A
Virtual World
– Most Challenging To Create
27/04/2015 Dr. Bashir/CPHHI/UH/2014
22. The PowerWall is a virtual reality system that displays large models in
accurate dimensions.
22Dr. Bashir/CPHHI/UH/2014
24. Virtual Reality Applications
• Medicine
–Pain And Anxiety; Examinations And
Diagnoses; Physical Therapy
• Education And Training
–Virtual School Trips, Military Training
27/04/2015 24Dr. Bashir/CPHHI/UH/2014
25. What Developments
• Support for Patient Groups
• Telemedicine
– Outreach Monitoring – Lifeshirt and others
• Human Factors Design
– Alarms and Displays – Preventing Medical Error
27/04/2015 25Dr. Bashir/CPHHI/UH/2014
26. Telemedicine
• Synergistic On New Non-medical Technologies
– Land-based Telephone Technology
– Mobile Telephone Technology
– Blue Tooth,.........wireless...........etc.
• Takes Different Forms
– Remote ECG Analysis
– Remote Consultation
27/04/2015 26Dr. Bashir/CPHHI/UH/2014
27. What is Telemedicine
•Telemedicine May Be Defined As
The Use Of Computers And
Telecommunication Technologies
To Provide Medical Information
And Services From Distant Locations
27/04/2015 27Dr. Bashir/CPHHI/UH/2014
28. Different Types Of Services
Telecardiology
Teleradiology
Telepathology
Tele psychiatry
Early Warning System
[ Prevention and control of endemic and infectious diseases ]
27/04/2015 28Dr. Bashir/CPHHI/UH/2014
29. Requirement Specification
Nodal Hospital
Referral Hospital
• A Patient Getting Treated
• A Doctor
• A Remote Telemedicine Console Having Audio Visual
And Data Conferencing Facilities
• An Expert/ Specialized Doctor
• A Central Telemedicine Server Having
Audio Visual And Data Conferencing Facility
27/04/2015 29Dr. Bashir/CPHHI/UH/2014
30. Sequence Of Operation
PATIENT IN
Patient visits OPD
Local Doctor checks up
Patient receives local treatment
and not referred to telemedicine
system
Patient referred to the Telemedicine system (some special
investigations may be suggested)
Patient visits Telemedicine data-entry console.
Operator entries patient record, data and images of test
results, appointment date is fixed for online telemedicine
session
OUT
OUT
Offline Data transfer
from Nodal Centre
27/04/2015 30Dr. Bashir/CPHHI/UH/2014
31. Sequence of Operation
Patient 1
Patient 2
Patient 3
Patient 4
.
.
.
Online conference for the patient.
Patient, local doctors at the nodal
hospital and specialist doctors at the
referral hospital
Patient Queue
IN OUT
27/04/2015 31Dr. Bashir/CPHHI/UH/2014
32. Hardware Configuration
Digital camera
Referral Hospital
Nodal Hospital
PSTN/ISDN/VSAT link
Scanner
PrinterModem
Modem
Microscope and other
medical instruments
Video Conference
Video Conference
Telephone
Telephone
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34. The Data
• Data Related To A Patient’s Personal
Information
• Data Related To A Patients Medical Information
• Data For Patient Management In Telemedicine
• Data Related To The Doctors
• Data For System Management
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35. Other Issues
•Incorporation of Standard.
•Health Level Seven (HL7)
•Digital Imaging Communication in Medicine
(DICOM)
•Data Security.
•Legal & Ethical Issue
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36. More Radical Approach
• Lifeshirt
– Set of sensors in a jacket
– Recorded on a PDA
– Analysed by software
remotely or at home
– Remote monitoring possible
– GPS
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37. Lifeshirt requires
• Online/automatic data interpretation
– Data-mining and machine learning
• Easy to use sensors
• Social framework of response
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38. Human Error
Cause of Critical Incident %
Human Factors Error 65.9
Fixation Error 20.5
Unknown Cause 10.6
Equipment Failure 3.0
Type of error %
Failure to check 43.9
Inexperience 41.0
Inattention 32.7
Fixation 20.5
Haste 25.8
Distraction 14.0
Fatigue 10.8
Not following procedure 6.1
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39. Lessons to be drawn
• Informatics solutions that are not ecological
will create more error
• Solutions that create more error will destroy
clinical confidence in informatics
• Solutions that destroy confidence in
informatics will not be used
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40. Conclusions
• Health Informatics will be very important in the
future
– Very varied
– Depend on Social underpinning
– Has to be appropriate not flash
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41. Conclusions
Our imagination is the only limit to what we can hope
to have in the future.
Charles F. Kettering
I have seen the future and it doesn't work.
Robert Fulford
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