Emerging Trends in Healthcare InnovationGokul Alex
A Point of View on Applying Innovation Incubation and Ecosystem Development to build a business innovation ecosystem in Healthcare sector with specific focus on Service Design and Service Innovation
Healthcare in Digital Age
by Assit. Prof. Polawat Witoolkollachit,MD
Present for the 3rd Samitivej Sriracha Medical Symposium 2018 "CQI & Innovation in Healthcare 4.0"
Emerging Trends in Healthcare InnovationGokul Alex
A Point of View on Applying Innovation Incubation and Ecosystem Development to build a business innovation ecosystem in Healthcare sector with specific focus on Service Design and Service Innovation
Healthcare in Digital Age
by Assit. Prof. Polawat Witoolkollachit,MD
Present for the 3rd Samitivej Sriracha Medical Symposium 2018 "CQI & Innovation in Healthcare 4.0"
Introduction to Health Informatics and Health Information Technology (Part 1)...Nawanan Theera-Ampornpunt
Presented at the Health Informatics and Health Information Technology Course, Doctor of Philosophy and Master of Science Programs in Data Science for Health Care (International Program), Faculty of Medicine Ramathibodi Hospital, Mahidol University on October 3, 2017
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
“People analytics” is a frequently used buzzword. But questions remain as to why this is becoming such a prominent challenge for HR. What are leading organizations doing to develop their understanding of how data analytics can drive better people decisions? In this session, learn what you can start doing tomorrow to accelerate and mobilize your people analytics efforts.
Learning Objectives
• Learn the research and trends in data & analytics.
• Learn what is driving the people analytics movement.
• Learn the barriers to entry for companies.
• Learn how to mobilize your efforts in building out your people & analytics capabilities.
Speaker: Diego Gomez, Vice President of Human Capital Management Transformation, Oracle
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball ApproachMark Ginnebaugh
Data Warehouse - Business Intelligence Lifecycle Overview by Warren Thronthwaite
This slide deck describes the Kimball approach from the best-selling Data Warehouse Toolkit, 2nd Edition. It was presented to the Bay Area Microsoft Business Intelligence User Group in October 2012.
Starting with business requirements and project definition, the lifecycle branches out into three tracks: Technical, Data and Applications. You will learn:
* The major steps in the Lifecycle and what needs to happen in each one.
* Why business requirements are so important and how they influence all major decisions across the entire DW/BI system.
* Key tools for prioritizing business requirements and creating an enterprise information framework.
* How to break up a DW/BI system into doable increments that add real business value and can be completed in a reasonable time frame.
Digital Healthcare Trends: Transformation Towards Better Care RelationshipKumaraguru Veerasamy
Digital health encompasses digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. With the increasing adoption of telemedicine, wearable devices, mobile health apps (especially during the recent COVID-19 pandemic) and VR/AR; digital health is poised to take healthcare forward.
In this webinar, Dale Sanders will provide a pragmatic, step-by-step, and measurable roadmap for the adoption of analytics in healthcare-- a roadmap that organizations can use to plot their strategy and evaluate vendors; and that vendors can use to develop their products. Attendees will have a chance to learn about:
1) The details of his eight-level model, 2) A brief introduction to the HIMSS/IIA DELTA Model, 3) The importance of permanent organizational teams to sustain improvements from analytic investments, 4) The process of curating and maturing data governance, and 5) The coordination of a data acquisition strategy with payment and reimbursement strategies
Presented at the 7th Healthcare CIO Program, Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand on July 8, 2016
Presented at the Data Science for Healthcare Graduate Programs, Section for Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 30, 2019
Introduction to Health Informatics and Health Information Technology (Part 1)...Nawanan Theera-Ampornpunt
Presented at the Health Informatics and Health Information Technology Course, Doctor of Philosophy and Master of Science Programs in Data Science for Health Care (International Program), Faculty of Medicine Ramathibodi Hospital, Mahidol University on October 3, 2017
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
“People analytics” is a frequently used buzzword. But questions remain as to why this is becoming such a prominent challenge for HR. What are leading organizations doing to develop their understanding of how data analytics can drive better people decisions? In this session, learn what you can start doing tomorrow to accelerate and mobilize your people analytics efforts.
Learning Objectives
• Learn the research and trends in data & analytics.
• Learn what is driving the people analytics movement.
• Learn the barriers to entry for companies.
• Learn how to mobilize your efforts in building out your people & analytics capabilities.
Speaker: Diego Gomez, Vice President of Human Capital Management Transformation, Oracle
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball ApproachMark Ginnebaugh
Data Warehouse - Business Intelligence Lifecycle Overview by Warren Thronthwaite
This slide deck describes the Kimball approach from the best-selling Data Warehouse Toolkit, 2nd Edition. It was presented to the Bay Area Microsoft Business Intelligence User Group in October 2012.
Starting with business requirements and project definition, the lifecycle branches out into three tracks: Technical, Data and Applications. You will learn:
* The major steps in the Lifecycle and what needs to happen in each one.
* Why business requirements are so important and how they influence all major decisions across the entire DW/BI system.
* Key tools for prioritizing business requirements and creating an enterprise information framework.
* How to break up a DW/BI system into doable increments that add real business value and can be completed in a reasonable time frame.
Digital Healthcare Trends: Transformation Towards Better Care RelationshipKumaraguru Veerasamy
Digital health encompasses digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. With the increasing adoption of telemedicine, wearable devices, mobile health apps (especially during the recent COVID-19 pandemic) and VR/AR; digital health is poised to take healthcare forward.
In this webinar, Dale Sanders will provide a pragmatic, step-by-step, and measurable roadmap for the adoption of analytics in healthcare-- a roadmap that organizations can use to plot their strategy and evaluate vendors; and that vendors can use to develop their products. Attendees will have a chance to learn about:
1) The details of his eight-level model, 2) A brief introduction to the HIMSS/IIA DELTA Model, 3) The importance of permanent organizational teams to sustain improvements from analytic investments, 4) The process of curating and maturing data governance, and 5) The coordination of a data acquisition strategy with payment and reimbursement strategies
Presented at the 7th Healthcare CIO Program, Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand on July 8, 2016
Presented at the Data Science for Healthcare Graduate Programs, Section for Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 30, 2019
Presented at the Executive Breakfast Forum EP.4 : The Future of Healthcare is Data, Digital User Group Association, Bangkok, Thailand on March 10, 2021
Health Information Exchange & Interoperability for Better Health Outcomes, an...Nawanan Theera-Ampornpunt
Presented at the Second Growing e-Health Expertise, Knowledge and Skills (GEEKS) Program, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand on August 7, 2019
Presented at the Life Sci. Level Up Challenge 2020, a Project to Promote and Incubate New Medical and Health Technology Researchers and Startups Promot 2020 by Thailand Center of Excellence for Life Sciences (Public Organization) (TCELS) and Srinakharinwirot University, Ministry of Higher Education, Science, Research and Innovation, Bangkok, Thailand on August 1, 2020.
Integrating Health Information for National Health Systems Reform (October 30...Nawanan Theera-Ampornpunt
Presented at the Health Systems Reform Workshop on Big Rock 1 "Ending Pandemics through Innovation Program" on October 30-31, 2021, Nonthaburi, Thailand
Presented at the BDMS Golden Jubilee Scientific Conference 2022 "BDMS Beyond 50 years: Looking towards the centennial," Bangkok Dusit Medical Services Public Company Limited (BDMS), Bangkok, Thailand on October 19, 2022
Presented at The Thai Medical Informatics Association Annual Conference and The National Conference on Medical Informatics (TMI-NCMedInfo) 2021, Bangkok, Thailand on November 26, 2021
Presented at the Master of Science Program in Medical Epidemiology and the Doctor of Philosophy Program in Clinical Epidemiology, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 25, 2021
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 15, 2021
Consumer Health Informatics, Mobile Health, and Social Media for Health: Part...Nawanan Theera-Ampornpunt
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 10, 2021
Consumer Health Informatics, Mobile Health, and Social Media for Health: Part...Nawanan Theera-Ampornpunt
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 10, 2021
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 8, 2021
Health Information Privacy and Security (November 8, 2021)
Disruption in Health care industry
1. 1
Disruption in Health Care Industry
Nawanan Theera-Ampornpunt, M.D., Ph.D.
May 10, 2019
www.SlideShare.net/Nawanan
2. 2
2003 M.D. (First-Class Honors)
2011 Ph.D. (Health Informatics), Univ. of Minnesota
Assistant Dean for Informatics
Lecturer, Section for Clinical Epidemiology & Biostatistics
Faculty of Medicine Ramathibodi Hospital
Mahidol University
Interests: Health IT for Quality of Care, Social Media
IT Management, Security & Privacy
nawanan.the@mahidol.ac.th
SlideShare.net/Nawanan
นวนรรน ธีระอัมพรพันธุ์ (Nawanan Theera-Ampornpunt)
Line ID: NawananT
Introduction
3. 3
What words come to mind when you hear...
Digital Health
Transformation
8. 8
“Big data is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it...”
-- Dan Ariely @danariely (2013)
Substitute “Big data” with “AI”, “Blockchain”, “IoT”
of your choice.
-- Nawanan Theera-Ampornpunt (2018)
9. 9
Hype vs. Hope
Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
12. 12
A Real-Life Personal Story of
My Failure (as a Doctor and as
a Son) in Misdiagnosing
My Mom
Would AI Help?
13. 13
• Nothing is certain in medicine &
health care
• Large variations exist in patient
presentations, clinical course,
underlying genetic codes, patient &
provider behaviors, biological
responses & social contexts
Why Clinical Judgment Is Still Necessary?
14. 14
• Most diseases are not diagnosed by
diagnostic criteria, but by patterns of
clinical presentation and perceived
likelihood of different diseases given
available information (differential
diagnoses)
• Human is good at pattern
recognition, while machine is good at
logic & computations
Why Clinical Judgment Is Still Necessary?
15. 15
• Machines are (at best) as good as
the input data
–Not everything can be digitized or
digitally acquired
–Not everything digitized is accurate
(“Garbage In, Garbage Out”)
• Experience, context & human touch
matters
Why Clinical Judgment Is Still Necessary?
22. 22
• Life-or-Death
• Difficult to automate human decisions
– Nature of business
– Many & varied stakeholders
– Evolving standards of care
• Fragmented, poorly-coordinated systems
• Large, ever-growing & changing body of
knowledge
• High volume, low resources, little time
Why Healthcare Isn’t (Yet) “Smart”?
24. 24
• “Don’t implement technology just for
technology’s sake.”
• “Don’t make use of excellent technology.
Make excellent use of technology.”
(Tangwongsan, Supachai. Personal communication, 2005.)
• “Health care IT is not a panacea for all that ails
medicine.” (Hersh, 2004)
Some “Smart” Quotes
37. 37
To treat & to care
for their patients
to their best
abilities, given
limited time &
resources
Image Source: http://en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)
What Clinicians Want?
38. 38
Why Aren’t We Talk About These Words?
http://hcca-act.blogspot.com/2011/07/reflections-on-patient-centred-care.html
39. 39
The Goal of Health Care
The answer is already obvious...
“Health”
“Care”
40. 40
• Safe
• Timely
• Effective
• Patient-Centered
• Efficient
• Equitable
Institute of Medicine, Committee on Quality of Health Care in America. Crossing the quality
chasm: a new health system for the 21st century. Washington, DC: National Academy
Press; 2001. 337 p.
High Quality Care
45. 45
• Humans are not perfect and are bound to
make errors
• Highlight problems in U.S. health care
system that systematically contributes to
medical errors and poor quality
• Recommends reform
• Health IT plays a role in improving patient
safety
Summary of These Reports
46. 46
Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/
(Right) http://graphics8.nytimes.com/images/2008/12/05/health/chen_600.jpg
To Err is Human 1: Attention
47. 47Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital
To Err is Human 2: Memory
48. 48
• Medication Errors
–Drug Allergies
–Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
Common Errors
50. 50
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
54. 54
Overview การปฏิรูปประเทศเรื่อง Health IT
Intra-Hospital IT
•Digital Health Records
(Electronic Health Records)
•Digital Transformation
•AI, Data Analytics
•Hospital IT Quality
Improvement (HA-IT)
Inter-Hospital IT
•Health Information
Exchange (HIE)
Extra-Hospital IT
•Personal Health
Records (PHRs) Patient
at Home
56. 56
Overview การปฏิรูปประเทศเรื่อง Health IT
Intra-Hospital IT
•Digital Health Records
(Electronic Health Records)
•Digital Transformation
•AI, Data Analytics
•Hospital IT Quality
Improvement (HA-IT)
Inter-Hospital IT
•Health Information
Exchange (HIE)
Extra-Hospital IT
•Personal Health
Records (PHRs) Patient
at Home
58. 58
Hospital A Hospital B
Clinic D
Policymakers
Patient at
Home
Hospital C
HIE Platform
Health Information Exchange (HIE)
59. 59
Areas of Health Informatics
Patients &
Consumers
Providers &
Patients
Healthcare
Managers, Policy-
Makers, Payers,
Epidemiologists,
Researchers
Copyright Nawanan Theera-Ampornpunt (2018)
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics
60. 60
Incarnations of Health IT
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics
HIS/CIS
EHRs
Computerized Physician
Order Entry (CPOE)
Clinical Decision
Support Systems
(CDS) (including AI)
Closed Loop
Medication
PACS/RIS
LIS
Nursing
Apps
Disease Surveillance
(Active/Passive)
Business
Intelligence &
Dashboards
Telemedicine
Real-time Syndromic
Surveillance
mHealth for Public
Health Workers &
Volunteers
PHRs
Health Information
Exchange (HIE)
eReferral
mHealth for
Consumers
Wearable
Devices
Social
Media
Copyright Nawanan Theera-Ampornpunt (2018)
61. 61
Where We Are Today...
Copyright Nawanan Theera-Ampornpunt (2018)
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics
Technology that
focuses on the sick,
not the healthy
Silos of data
within hospitalPoor/unstructured
data quality
Lack of health data
outside hospital
Poor data
integration across
hospitals/clinics
Poor data integration
for monitoring &
evaluation
Poor data quality (GIGO)
Finance leads
clinical outcomes
Poor IT change
management
Cybersecurity
& privacy risks
Few real examples
of precision
medicine
Little access
to own
health data
Poor patient
engagement
Poor accuracy
of wearables Lack of evidence
for health values
Health literacy
Information
Behavioral
change
Few standards
Lack of health IT
governance
64. 64
• Telemedicine (โทรเวชกรรม)
• tele- (to or at a distance) + medicine/health
• “The use of telecommunications technology for
medical diagnostic, monitoring and therapeutic
purposes when distance and/or time separates the
participants.”
• Some use telehealth to indicate care beyond
provided in medical encounters (e.g. health
education, health-related websites)
Telemedicine & Telehealth
Hersh et al (2006)
66. 66
• Store-and-forward telemedicine
– Collect data then transmit them for subsequent interpretation
• Home-based telemedicine
– Used by health professionals to monitor physiology, test results,
images and sounds, usually collected in a patient’s home or a
nursing facility
• Office/hospital-based telemedicine
– Usually real-time clinician-patient interactions that conventionally
would require face-to-face encounters between a patient and a
health professional
3 Main Types of Telemedicine
Hersh et al (2006)
68. 68
• Current Trends
–Change from using telehealth to increase access to
health care to providing convenience and reducing
cost
–Expansion of telehealth to chronic conditions
–Migration of telehealth from hospitals and clinics to
home and mobile devices
State of TeleHealth
Dorsey & Topol (2015)
69. 69
• Limitations of Telehealth
–Reimbursement
• Limited & fragmented insurance coverage of telehealth
• Potential for excess health care utilization
–Clinical issues
• Patient-physician relationship
• Quality of physical examination
• Quality of care with remote visits than with in-person
visits
State of TeleHealth
Dorsey & Topol (2015)
70. 70Dorsey & Topol (2015)
• Limitations of Telehealth
–Clinical issues (continued)
•Potential for abuse (e.g. overprescribing of
narcotics)
•Fragmentation of care among multiple providers
–Legal issues (e.g., state licensure, liability)
–Social issues (digital divide)
State of TeleHealth
71. 71
Legal Issues
• Privacy & Security Issues
• Liability due to Risks of Medical Errors
▪ Misdiagnosis
▪ Delayed Diagnosis & Treatment
▪ Miscommunication & Information Reliability
• Legal Compliance
▪ พรบ.สถานพยาบาล, พรบ.วิชาชีพ, พรบ.ยา, พรบ.เครื่องมือ
แพทย์ ฯลฯ
▪ Legal Documentation
89. 89
• Disease-specific & specialty-specific telemedicine
medical practice guidelines (review of evidence &
balancing risk-benefits)
• Medical records documentation guidelines
• Security, privacy & identification/authentication
standards
• Harmonization among regulators
• Professional telemedicine training/certification?
Next Steps on Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
90. 90
• ความจาเป็นในการแก้ไข พรบ.วิชาชีพเวชกรรม หรือกฎหมาย
อื่นๆ ที่เกี่ยวข้อง หรือออก พรบ. Telemedicine ยังไม่ชัดเจน
แต่มีความพยายามที่จะหารือกันระหว่างหน่วยงานต่างๆ
• Telemedicine Providers เริ่มเปิดให้บริการแล้ว ต้อง
monitor case ร้องเรียน หรือปัญหาที่เกิดขึ้น
Next Steps on Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
93. 93
Myths
• We don’t need standards
• Standards are IT people’s jobs
• We should exclude vendors from this
• We need the same software to share data
• We need to always adopt international
standards
• We need to always use local standards
Theera-Ampornpunt (2011)
Myths & Truths about Standards
94. 94
Being Smart #5:
Go for Systems that Use
Standards, Not a Unified,
Conquer-the-World System
Image Source: https://www.businessinsider.in/google-let-users-play-with-thanos-destructive-
power/articleshow/69054170.cms
95. 95
• CDS as a replacement or supplement of
clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
The “Fundamental Theorem” Model
Friedman (2009)
Wrong Assumption
Correct Assumption
Clinical Decision Support Systems (CDS)
97. 97
Overview การปฏิรูปประเทศเรื่อง Health IT
Intra-Hospital IT
•Digital Health Records
(Electronic Health Records)
•Digital Transformation
•AI, Data Analytics
•Hospital IT Quality
Improvement (HA-IT)
Inter-Hospital IT
•Health Information
Exchange (HIE)
Extra-Hospital IT
•Personal Health
Records (PHRs) Patient
at Home
98. 98
• โรงพยาบาลจะพัฒนาระบบสารสนเทศของตัวเองให้เข้มแข็งได้อย่างไร
• เมื่อไรข้อมูลผู้ป่วยจะเชื่อมถึงกันได้ระหว่างโรงพยาบาลต่างๆ โดยเฉพาะข้ามสังกัด
และนอก สธ. // ควรมีซอฟต์แวร์เดียวใช้ทั้งประเทศหรือไม่
• มาตรฐานข้อมูล 43 แฟ้มไม่ครอบคลุมการใช้งาน จะปรับปรุงได้อย่างไร
• จะเลือกมาตรฐานข้อมูลยาที่เหมาะสมมาใช้งานใน รพ. ได้อย่างไร
• โครงสร้างการออกแบบ Data Center ระดับจังหวัด, เขต และประเทศ ควรเป็น
อย่างไร (Centralized, Decentralized, Distributed, Hybrid, etc.)
• การนาข้อมูลระดับ รพ. มาใช้ประโยชน์ในระดับจังหวัด เขต และประเทศ ควรเป็น
อย่างไร และออกแบบ Infrastructure Data Model อย่างไร
• Disruptive technology (AI, Blockchain, etc.) ควรมีบทบาทใน Healthcare
ไทยอย่างไร มากกว่าการเป็น buzzwords
• ความก้าวหน้าและยั่งยืนของกาลังคนด้าน Health IT เมื่อไรจะได้รับการแก้ไข
คาถามที่เจอบ่อยๆ ในวงการ Health IT ไทย
99. 99
• ระบบข้อมูลการส่งต่อผู้ป่วยควรใช้โปรแกรมใด
• ระบบ IT การแพทย์ฉุกเฉินมีข้อจากัดการเชื่อมต่อ
• ระบบ IT PCC ควรเป็นอย่างไร
• ทาอย่างไรจึงจะมีระบบ PHRs ที่ครอบคลุมผู้ป่วยส่วนใหญ่ มีการใช้
ประโยชน์อย่างเต็มที่
• ข้อมูล Precision Medicine & Genomics จะ integrate ในการดูแล
ผู้ป่วยอย่างไร
• ข้อเสนอ: ควรเป็น Platform เดียวกัน
คาถามที่เจอบ่อยๆ ในวงการ Health IT ไทย