FUNCTIONAL GENOMICS AND EXPERIMENTAL MEDICINEVenue:
Guest Speakers:
Organized and hosted by National Health Research Institutes Forum
Sponsored by Ministry of Health and Welfare and Institute of Molecular and
Genomic Medicine, NHRI
Chi-Hsiung (Ed) Wang (NorthShore University HealthSystem Research Institute)
Kwang-Jen Hsiao (Preventive Medicine Foundation)
Po-Huang Chiang (National Health Research Institutes)
Yu-Chuan Li (Taipei Medical University)
Opening Remarks
Welcome to the Learning Health Systems Workshop. This is an event organized by National
Health Research Institutes Forum.
As a leader of NHRI Forum, I am very pleased to invite and introduce to you two outstanding
scholars from USA, Professor Charles P. Friedman (University of Michigan, Ann Arbor, MI)
and Professor Ed. Wang (NorthShore University HealthSystem Research Institute, Evanston,
IL) to participate in this workshop. Additionally, we have invited local experts to share their
views on the projects related to Learning Health Systems (LHS). Our goal for this in this half-
day meeting, is that, we wish to promote the spirit of data sharing, and the use of right tools
and methods to maximize our research efforts and outcomes. We hope that the NHRI Forum,
now the third year, after my tenure, can provide you our experiences in building the
infrastructures to improve the operation of our healthcare systems.
At NHRI, we have determined to focus our attention on precision medicine and LHS. It would
be a high priority that we learn from this workshop and to collaborate, locally and
internationally, to achieve the common goal of quality healthcare at low cost.
I hope you will enjoy the lectures, making new friends and have a good day in Taipei.
Cheng-Wen Wu, M.D., Ph.D. Chairman, NHRI Forum, Taipei, Taiwan
-2-
-3-
Part I: Learning Health Systems, Methods and Shih-Feng Tsai*
Applications
Health System
Performance Outcomes
and Future
Kwang-Jen Hsiao
Prediction and Prevention
12:10-12:25 Discussion
12:30-13:30 Lunch
-4-
Chair, Learning Health Sciences, University of Michigan, MI, United States
Josiah Macy Jr. Professor, Medical Education, University of Michigan, MI, United States
Education:
Career:
Award:
Publication:
Friedman CP. A‘fundamental theorem’ of biomedical informatics. Journal of the American
Medical Informatics Association. 2009. 16: 169-170.
Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system.
Science Translational Medicine. 2010. 2: 1–3.
Pageler NM, Friedman CP, Longhurst CA. Refocusing medical education in the EMR era.
JAMA. 2013. 310: 2249-50.
Friedman CP, Rubin JC, Brown J, et al. Toward a Science of Learning Systems: A Research
Agenda for the High-Functioning Learning Health System. Journal of the American Medical
Informatics Association. 22: 43-50, 2015.
E-mail: cpfried@umich.edu
2011-present Professor (with tenure), Schools of Information and Public Health
Director, Health Informatics Program, University of Michigan
2009-2011 Chief Scientific Officer, Office of the National Coordinator for Health
Information Technology, Office of the Secretary, U.S. Department of
Health and Human Services
Department of Health and Human Services
Ph.D.
M.S.
B.S.
Physics, Massachusetts Institute of Technology
Physics, Massachusetts Institute of Technology
2010-present
2009-present
Advisor to the Organization for Economic Co-operation and
Development (OECD)
HealthSystem, Evanston, IL, United States
Education:
Career:
Award:
Publication:
Yao K, Belkora J, Sisco M, Rosenberg S, Bedrosian I, Liederbach E, Wang CH (2016).
Survey of the Deficits in Surgeons' Knowledge of Contralateral Prophylactic Mastectomy.
JAMA Surg. 2016 Apr 1; 151(4):391-3. PMID: 26606424
Suman P, Wang CH, Abadin SS, Block R, Raghavan V, Moo-Young TA, Prinz RA,
Winchester DJ (2016). Timing of Radioactive Iodine Therapy Does not Impact Overall
Survival in High-Risk Papiliary Thyroid Carcinoma. Endocr Pract. 2016 Mar 28. [Epub
ahead of print]. PMID: 27018620
Suman P, Wang CH, Abadin SS, Moo-Young TA, Prinz RA, Winchester DJ. (2016). Risk
factors for central lymph node metastasis in papillary thyroid carcinoma: A National
Cancer Data Base (NCDB) study. Surgery. 2016 Jan;159(1):31-40. PMID: 26435436
E-mail: cwang3@northshore.org
Biostatistics, Case Western Reserve University
Science in Social Work, State University of New York at Buffalo
Science in Industrial Engineering, State University of New York at Buffalo
Science in Industrial Engineering, Tunghai University
2015-present Director of Biostatistics, Center for Biomedical Research Informatics,
NorthShore University HealthSystem
NorthShore University HealthSystem
HealthSystem
2006
2003
Methodologies, San Francisco
Toronto
-6-
Adjunct Research Investigator, Department of Medical Research, Taipei Veterans General Hospital,
Taipei, Taiwan
Education:
Career:
Award:
Publication:
Tsao PC, Shiau YS, Chiang SH, Ho HC, Liu YL, Chung YF, Lin LJ, Chen MR, Chang JK,
Soong WJ, Lin HL, Hwang B, Hsiao KJ. Development of a Newborn Screening Program for
Critical Congenital Heart Disease (CCHD) in Taipei. PLoS One 2016;11:e0153407. DOI:
10.1371/journal.pone.0153407
Liu YN, Liu TT, Fan YL, Niu DM, Chien YH, Chou YY, Lee NC, Hsiao KJ, Chiu YH.
Measuring propionyl-CoA carboxylase activity in phytohemagglutinin stimulated lymphocytes
using high performance liquid chromatography. Clin Chim Acta 2016;453:13-20. DOI:
10.1016/j.cca.2015.11.023
Verma J, Thomas DC, Sharma S, Jhingan G, Singh A, Hsiao KJ, Schoonderwoerd K, Puri
RD, Verma IC. Inherited metabolic disorders: Quality management for laboratory diagnosis.
Clin Chim Acta 2015;447:1-7. DOI: 10.1016/j.cca.2015.04.040
E-mail: hsiao@pmf.tw
Biomedical Sciences, Mount Sinai School of Medicine, City University of
New York
Outstanding Contribution Award for Newborn Screening in China, Shanghai
Newborn Screening Program
Health Medal of the Second Order, Department of Health, ROC
Outstanding Contribution Award
1990-present Adjunct Investigator, Department of Medical Research, Taipei Veterans
General Hospital (VGH)
-7-
Miaoli, Taiwan
Education:
Career:
Award:
Publication:
Lo FE*, Lu PJ, Tsai MK, Lee JH, Wen C, Wen CP**, Wai JPM, Tsao CK, Chiang PH, Lyu SY,
Ma KL, Chi YC, Li CS, Liu CC, Wu XF. The role of physical activity in harm reduction among
betel quid chewers from a prospective cohort of 419,378 individuals. PLoS One, Apr
4;11(4):e0152246.
Chan TC, Wang HW, Tseng TJ, Chiang PH*. Spatial clustering and local risk factors of
chronic obstructive pulmonary disease (COPD). International Journal of Environmental
Research and Public Health. 2015 Dec 10;12(12):15716-15727.
Tsou HC, Yeh HL, Chiang PH* (Corresponding author). Spatial trend analysis of dengue
fever outbreak The Journal of Taiwan Association for Medical Informatics. 2015 Dec 24(4):
39-48.
Chan TC, Fan IC, Liu MSY, Su MD, Chiang PH* (Corresponding author). Addressing Health
Disparities in Chronic Kidney Disease. International Journal of Environmental Research and
Public Health. 11(12), 12848-12865; doi:10.3390/ijerph111212848. December 2014.
E-mail: chiangp@nhri.org.tw
2015
2015
2014
Poster Competition Award (Taiwan Public Health Association)
Contribution Award (Taipei City Government)
2016-present Associate Director, National Health Research Institutes Forum
2016-present Associate Investigator, Institute of Population Health Sciences, National
Health Research Institutes
University
-8-
Taipei, Taiwan
Dean, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
Chair, Dept. of Dermatology, Taipei Medical University. Wan Fang Hospital, Taipei, Taiwan
Education:
Career:
Award:
Publication:
Usman Iqbal, Chun-Kung Hsu, Phung Anh (Alex) Nguyen, Daniel Livius Clinciu, Richard Lu,
Shabbir Syed-Abdul, Hsuan-Chia Yang, Yao-Chin Wang, Chu-Ya Huang, Chih-Wei Huang, Yo-
Cheng Chang, Min-Huei Hsu, Wen-Shan Jian, Yu-Chuan (Jack) Li* Cancer-Disease
Associations: A Visualization and Animation through Medical Big Data.
Computer Methods and Programs in Biomedicine. 2016 Apr;127:44-51.
Chih-Wei Huang+, Shabbir Syed-Abdul, Wen-Shan Jian, Usman Iqbal, Phung-Anh (Alex)
Nguyen, Peisan Lee, Shen-Hsien Lin, Wen-Ding Hsu, Mai-Szu Wu, Chun-Fu Wang+, Kwan-Liu
Ma*, Yu-Chuan (Jack) Li* A Novel Tool For Visualizing Chronic Kidney Disease Associated
Polymorbidity: A 13-Year Cohort Study In Taiwan. Journal of the American Medical Informatics
Association. 2015 Mar;22(2):290-8
Shabbir SA, Lin CW, Scholl J, Luis Fernandez-Luque, Jian WS, Hsu MH, Liou DM, ,Li YC*.
Facebook use leads to health-care reform in Taiwan. The Lancet. 2011 Jun 18.
377(9783):2083-4
Medicine, Taipei Medical University, Taipei, Taiwan
2010
2010
2001
2014 Editor-in-Chief, International Journal for Quality in Health Care (IJQHC)
2011 Dean, College of Medical Science and Technology, Taipei Medical
University, Taiwan
2009 Chair Dermatologist, Dept. of Dermatology, Taipei Medical University. Wan
Fang Hospital, Taiwan
University, Taiwan
The Knowledge Grid: A "Brain" for the Learning Health System
Charles P. Friedman Chair, Department of Learning Health Sciences, University of Michigan Medical School, U.S.A.
There is currently enormous global interest in informatics platforms supporting Big Data and
Precision Medicine. Unfortunately, almost all of the platforms that have been deployed, or
are under development, lack several critical components and services. Specifically, these
platforms very strongly support data acquisition, aggregation, and analysis activities that
generate new knowledge. But after new knowledge is created, these platforms provide no
place for the new knowledge to live: no place where this knowledge can be stored and
curated, and from which the knowledge can be shared and rapidly applied to change health
care practices. As such, these Big Data and Precision Medicine platforms support a Learning
Health System without a "brain" to remember what the system has learned.
I will begin this presentation by reviewing the concepts fundamental to the Learning Health
System, and relating these concepts to Big Data and Precision Medicine. I will then
introduce and describe the Knowledge Grid, which includes components of an informatics
platform designed capture the knowledge developed from analytics, store this knowledge in
computable forms, enable it to be shared, and generate from it specific advice designed to
improve health care practice. The Knowledge Grid exists as a working prototype, and will be
ready for trial use in the Fall of 2016.
I will argue that, in the absence of a "brain" provided by the Knowledge Grid, Big Data and
Precision Medicine will inevitably fail to achieve their vaunted potential.
-9-
Outcomes
Chi-Hiung (Ed), Wang Director of Biostatistics, Center for Biomedical Research Informatics, NorthShore University HealthSystem,
Evanston, U.S.A.
Insurance payment models in the United States are changing rapidly to address the rapid
rising health care costs. The hospital Value-Based Purchasing (VBP) program, established
by the Affordable Care Act (ACA), requires a pay-for-performance approach to determine
payment based on care quality provided by each hospital. Under VBP, Medicare will make
incentive payments to hospitals based on performance measures. It remains challenging,
however, as how to develop a statistical methodology to provide unbiased assessment for
hospitals across different geographic settings with great variability in patient volume and risk
factors. Using the data from the National Cancer Data Base registry, we will demonstrate
how to use a multilevel Cox regression nested model to provide an unbiased evaluation of
patient survival outcome among 24,000+ gastric cancer patients in 1,203 hospitals across the
United States. In the era of big data and learning health system, this presentation will
illustrate how to use predictive analytics to assess quality of care and ultimately to improve
patient outcomes.
2. -







1
2
4

2
Population : 23M 314M
GDP per capita : USD 20,328 51,456 GDP (PPP) : USD 38,749 51,456 NHE / GDP : 6.51 % 17%
Life Expectancy : 76.0 (male) 76 82.6 (female) 81
Infant Mortality : 3.7 ‰ 6.06 ‰ Newborn Mortality : 2.43 ‰ 4.04 ‰ 2012 data
Congenital Hypothyroidism Homocystinuria Phenylketonuria (BH4 Def.)
Treatable Congenital Metabolic DiseasesTreatable Congenital Metabolic Diseases
1. The condition sought should be an important health problem.
2. There should be an accepted treatment for patients with recognized disease.
3. Facilities for diagnosis and treatment should be available.
4. There should be a recognizable latent or early symptomatic state.
5. There should be a suitable test or examination.
6. The test or examination should be acceptable to the population.
7. The natural history of the condition, including development from latent to declared disease, should be adequately understood.
8. There should be an agreed policy on whom to treat as patients.
9. The cost of case finding (including diagnosis and treatment of patients diagnosed) should be economically balanced in relation to possible expenditure on medical care as a whole.
10. Case-finding should be a continuing process and not a “once and for all” project.
Principles and Practice of Screening for Disease — Wilson and Jungner, 1968 —
1. Wilson JMG, Junger F. Principles and practice of screening for disease. WHO, 1968; 26-39 2. Andermann A et al. Revisiting Wilson and Jungner in the genomic age…. Bull WHO 2008:86:317
Treatable
Available a reliable test (suitable for mass screening)
Acceptable benefit vs cost ratio
Criteria for Neonatal Screening
When We Started in 1982
University of Hamburg
Medical Students in Taipei County (1982.4)
1). Hsiao KJ et al. J Formosan Med Assoc 1989;88:18 2). Wuu KD et al. Jpn J Human Genet 1988;33:33
12
Routine Neonatal Screening in TaiwanRoutine Neonatal Screening in Taiwan Routine Screening Items (1984 ~ 2006.7 )
Congenital Hypothyroidism ( CHT )
Neonatal Screening Network
All the delivery units 3 Screening centers 19 Referral hospitals Public health nurse follow-up system
Taipei
Quemoy
50 10
Hsiao KJ, Wuu KD. Proc 4th Asian-Pacific Congress of Clin Biochem. Hong Kong: 1989;214-8.
Neonatal Screening Coverage Rate in Taiwan
9
Disease No. of patient
G6PD deficiency 80,203 80,119 (?) 0 0 4 0
Outcome of Cases Detected by Neonatal Screening in Taiwan
Outcome of Cases Detected by Neonatal Screening in Taiwan
* data from follow up center, neonatal screening centers and pediatric specialists
2005.11
( 1984 ~ 2004 )
Chiang SH, Hsiao KJ. In: Lin SJ ed. Genetic Health and Prevention of Rare Diseases. Taipei: Bureau of Health Promotion, DOH 2007; pp.63-94
I. Keep All the Routine Items − CHT ( TSH ) − PKU ( Phe ) − HCU ( Met ) − GAL ( galactose ) − G6PD ( g6pd )
II. New Routine Screening Items (started 2006.7.1) − Congenital Adrenal Hyperplasia (CAH) ( 17-OHP )
− Maple Syrup Urine Disease (MSUD) ( Leu/Ile, Val )
− Medium Chain Acyl-CoA Dehydrogenase Def. (MCAD) ( C6, C8, C10 ) − Glutaric Aciduria Type I (GA I) ( C5DC ) − Isovaleric Acidemia (IVA) ( C5 ) − Methylmalonic Aciduria (MMA) ( C3 )
Recommendations for Adjustments (2004.4)Recommendations for Adjustments (2004.4)
http://nbs.tw
S
Chiang SH, Shiao YS, Hsiao KJ. Prospective of neonatal screening. I n: Lee ML ed. Metabolic Disorders: Taiwan Experience. Taipei: Department of Health. 2004; 29-52.
M S/M
S
Suggestion for the Adjustment of the Target Time for the Screening System
Suggestion for the Adjustment of the Target Time for the Screening System
0 48 120              168     192                  312 (hour)
Sample Collected
Present Target Time ( before 2006.7 )
0 24 72 96 112 168 (hour)
Sample Collected SendBirth Received
Adjusted Target Time ( including holidays )
* Items : CAH, MS/MS, G6PD, GAL 2004.4
13
13
Coverage − > 99% (1997~ )
Sample Collection − 99% samples are collected within 3 days after birth − 99% samples reached the screening center within 2 days after collection
Laboratory Testing − 99.9% results reported within 3 days after received
Follow-up − PKUCHT ...10 items: total positive rate 6.0% , follow-up rate 98% − G6PD: positive rate 2.7%, follow-up 97%
Diagnosis and Treatment − 91.3% confirmed case diagnosed and treated within 1 month after birth
Incidence of Congenital Metabolic Diseases Detected by Newborn Screening in Taiwan
Disease CAH MSUD MCAD MMA IVA GA-I
rate 1/15,000 1/100,000 1/330,000 1/100,000 1/660,000 1/100,000
Disease PKU CHT HCU GAL G6PD
rate 1/30,000 1/2,000 1/360,000 1/160,000 1/50
14
1. Statistic data, Health Promotion Administration, Ministry of Health and Welfare (2014) 2. Niu DM et al. Nationwide survey of extended newborn screening by tandem mass spectrometry in
Taiwan. J Inherit Metab Dis. 2010;33(Suppl 2):S295-305. ( n = 1,495,132 )
German and UK Routine Screening Items
15
2004 German Federal Government limit routine items − CHT, CAH, GAL, Biotinidase, (CF as 2011?) − following 10 MS/MS items (any other items suppressed and not reported)
British NHS routine screening items − 2011 PKU, CHT, MCAD, CF, and Sickle Cell Dis (SCD) − 2015 expended 4 MS/MS items ( MSUD, HCU, GA1, IVA)
Prollitt RJ. J Inherit Metab Dis 2006:29:390
Loeber GJ et al. J Inherit Metab Dis 2012;35:603
Other MS/MS Items Maybe Expended in Taiwan
Biotinidase Deficiency (MS/MS sensitivity not enough)
Citrullinemia I, II  (Citrin Deficiency*, Sensitivity? )
Propionic acidemia (PA)
Multiple Carboxylase Deficiency (MCD) ( Sensitivity? )
Glutaric aciduria type II II (GA II, MADD*) ( Sensitivity? )
Carnitine Uptake Defect * (CUD, PCD) ( Sensitivity? )
Very-long-chain acyl- CoA dehydrogenase deficiency (VLCAD) ( Sensitivity? )
− Carnitine palmitoyltransferase deficiency type II (CPT II) (?)
− 3-Methylcrotonyl-CoA Carboxylase Def. (3-MCC) (?)
Severe Combine Immune Deficiency ( SCID )
17
T-cell receptor excision circles (TRECs) Quantitative PCR test 2010 pilot project started in Taiwan − Incidence detected 1/50,000 ~ 1/80,000 − Therapy: cord blood/stem cell transplantation ( 2 ~ 5 month old ) − Other diseases: persistent T cell lymphopenia, DiGeorge Syndrom, Down
Syndrome − Current coverage: 85 ~ 95 % (self pay NT$ 150.- ~ 250.- )
Chien YC et al. J Formosan Med Assoc 2015;114:12
van Zelm MC et al. Front Immunol 2011:012 Boy in the Bubble Syndrome
Lysosomal Storage Diseases (LSD)
1991 …… 2001 2003 2006 2007 …… 2012, in progress
Type I Gaucher Fabry MPS I Pompe MPS VI MPS II
Niemann-Pick MPS IVA……
Annu Rev Genomics Hum Genet 2012; 13:307
Glycogen Storage Disease − Pompe − ….. Sphingolipidosis − Gaucher − Fabry − Niemann-Pick − Krabbe − ……. Mucopolysaccharidosis (MPS) − I (Hurler, Scheie) − II (Hunter) − III, IV, VI, VII, IX
14
Incidence ( n = 1M ) − Infantile (IOPD): 1/40,000~1/55,000
− Late onset (LOPD): 1/18,000~1/25,000
− Pseudodeficiency: c.1726G>A (allele freq. 14.5%)
Therapy: ERT (IOPD treatment age < 1 month , median 9~14 day)
Current Coverage: 85 ~ 95 % (Self pay NT$ 150.-)
Chiang SC et al. Mol Genet Metab 2012;106:281 Yang CF et al. Am J Med Genet Part A 2013;164A:54
Urinary Glc4
Chien YH et al. J Pediatr 2015:166:985
Fabry Disease
Flour. Enz method Enzymic MS/MS method (2010)
Incidence ( n = 0.5M ) − Classical type: 1/15,000~1/25,000
− Cardiac type: 1/3,000 (male ~1/1,600, IVS4+919G>A)
− Pseudodeficiency: ? (D313Y)
Current Coverage: 85 ~ 95 % (Self pay NT$ 150.-)
Lin HY et al. Circ Cardiovasc Genet 2009;2:450 Liao HC et al. Clin Chim Acta 2014;431:80
Mucopolysaccharidosis I (MPS I)
22
2008 pilot project started in Taiwan Flour. Enz method Enzymic MS/MS method (2012) Incidence ( n = 0.1 M ) − 1/18,000~1/50,000 − I-H, I-HS, I-S types (?)
Treatment: ERT Current Coverage: 85 ~ 95 % (Self pay NT$ 150.-) Lin SP et al. Orphanet J Rare Dis 2013:8:147
Liao HC et al. Clin Chim Acta 2014;431:80
Gaucher Disease 2011 pilot project started in Taiwan Enzymic MS/MS method Incidence ( n = 0.1 M ) − 1/100,000 − Type 1, 2, or 3 (?)
Treatment: ERT for type 1 (nonneuronopathic), type 3 (?) Current Coverage: 85 ~ 95 % (Self pay NT$ 150.-) Liao HC et al. Clin Chim Acta 2014;431:80
Other Lysosomal Diseases (LSD)
PE MS/MS kit in developing
Self pay
Pilot Taiwan
2014Year
1.74 2.08
2.61 3.04
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Total budget for Hemophilia & other Rare Dis.
Total expenditures of Hemophilia
1.37% Total NHI budget
MPS IVa Listed in 2014.12
* National Health Insurance Administration, 2014 Statistics
NHI Cost of Treatment
Selfpay: HSCT / Cord Blood Transplantation ( NTD$ 0.9 ~ 1.3 M / case )
Enzyme Replacement Therapy
Other Possible Target Diseases
Guanidinoacetate Methyltransferase (GAMT) − Guanidinoacetate (MS/MS)
X-Linked Adrenoleukodystrophy (X-ALD) − Lysophosphatidylcholine (MS/MS)
Niemann-Pick Disease Type C (NPDc) − Bile Acids/Oxysterols (MS/MS)
Newborn Screening without Blood Spot
27
Biliary Atracia − Incidence ~ 1.51/10,000 − Stool Color Card , SCC (<1 month) − National program started in 2004 − Total case detected 93 ~ 97%
Lien TH et al. Hepatology 2011;53:202
Hearing Loss − Incidence 1.5 ~ 1.9/1,000 − aABR (1~3 days) − National program started in 2012 − Coverage ~ 97%
Huang HM et al. Int J Pediatr Otorhinolaryngol 2013;77:1734
Critical Congenital Heart Diseases − Incidence 0.8 ~ 1.5% − Pulse Oximetry (1~2 days) − Taipei City program started in 2013 − Coverage ~ 97%
Tsao PC et al. PLoS ONE 2016;11:e015340
Problems and Difficulties
Gallant NM et al. Mol Genet Metab 2012;106:55;
Possible harms of newborn screening − Physical burden to infants − psychosocial and logistic burdens to families from screening or diagnostic − increased risk of medical treatment for infants diagnosed earlier than
children with clinical presentation − delayed diagnosis from false negative results − Psychosocial harm from false positive results − uncertainty of clinical diagnosis, age of onset or clinical spectrum − Disparities in access to diagnosis or therapy
Goldenberg AJ et al. Matern Child Health J 2016;20:693
Is the selfpay items conform to “Health Equality” principal? − Declaration of Alma Ata.
WHO International conference on primary health care. 1978.
Overdiagnosis
29
30
Analyzed 874 gene in 589,306 genome sequences
13 adults harboring mutations for 8 sever Mendelian conditions, with no clinical manifestation
Incomplete penetrance of Mendelian disease is more common than we thought
Screen out case without syndrome Treat or not to treat?
Genotype Bichem Phenotype Clinical Phenotype
Diagnosis, Treatment
16
The IOM Committee on Assessing Genetic Risks recommended: “Newborn screening only take place 1) for conditions for which there are indications of clear benefit to the newborn, 2) when a system is in place for confirmatory diagnosis, and 3) when treatment and follow-up are available for affected newborns. . ……….”
Andrews LB, et al, eds. Assessin Genetic Risks. Implications for Health and Social Policy. Washington DC: National Academy of Sciences; 1994

:
Prevention
Taiwan
The urinary tract system is a major excretion system composed of the kidneys, ureters, and
the bladder. Different types of cancer of the urinary tract arise from the renal parenchyma
(renal cell carcinoma, RCC), and the urothelium, including upper tract urothelial carcinoma
(UTUC) and bladder cancer (BC). UTUC is a rare cancer in the world, but the incidence of
UTUC is unusually high and steadily increasing in Taiwan, and it is a disease of significant
morbidity and mortality in Southern Taiwan. Clinical observations have uncovered that UTUC
as well as renal cell carcinoma (RCC) prevail among patients with renal insufficiency. Taiwan
has the highest incidence and prevalence of end-stage renal disease in the world. The
pathogenesis of the UTUC involves the interactions of genetic and environmental factors. To
meet this challenge, NHRI Forum has sponsored a project to form an integrated study team
within NHRI and in collaboration with the urologists to investigate the epidemiology,
environmental and genetic factors, so that we can identify the risk factors for the prevention
of this emerging disease in Taiwan. Our long-term goal is to prevent the disease and to
improve therapeutic outcome of urothelial cancers. This program consists of three
components: 1. epidemiology of UTUC in southern Taiwan; 2. environmental factors and
tryptophan catabolism in urothelial cancers; 3. comparative genomics of RCC, UTUC, and
BC. Our integrated project is based on the hypothesis that exposure to unknown
environmental factors increases the risk of developing UTUC in susceptible individuals. I shall
present the status of the tissue bank, data source center, and preliminary results on
developing genetic tests for UTUC.
-18-
Yu-Chuan Li Professor, Graduate Institute of Biomedical Informatics, CoMST, Taipei Medical University, Taipei, Taiwan
Dean, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
Chair, Dept. of Dermatology, Taipei Medical University. Wan Fang Hospital, Taipei, Taiwan
Taiwan's Health Information Technology development has come a long way from the Health
Information Network that connects only 30 public hospitals in 1988 to a full-blown VPN that
connects all 500 hospitals and 20,000 clinics public and private. The current trend in HIT is
to move into Cloud and Big Data domains. The Taiwan Health Cloud is divided Wellness,
Prevention, Care and Medical Cloud. The Health Big Data strategy is to move from Open
data to Collectives data and eventually to My data where all citizens can access their own
medical record. The newest approach from the National Health Insurance agency has
provided more than one million downloads of My Health Bank, through which users can
access their own diagnosis, drugs, lab and procedures data done in any hospitals or clinics in
Taiwan.
-19-