1. EVALUATION AND MONITORING OF META-
ANALYSIS IN MEDICAL RESEARCH THROUGH
INFORMATION TECHNOLOGY
Mayadhar Barik, Doctorial Candidate, Department of Nuclear Medicine
All India Institute of Medical Sciences, New Delhi-110029
Email: mayadharbarik@gmail.com
Sushil K Meher, Department of Computer Facility
All India Institute of Medical Sciences, New Delhi-110029

2. INTRODUCTION
• Monitoring and evaluation in the field of health care are
much more important and closely related
• They are undertaken to find out the result of a research
project in relation to the clinical benefits, risk factors
and its importance for society

3. OBJECTIVE
• Its efficiency, effectiveness, sustainability and
reproducibility and hence play a crucial role in
medical research and education

4. MATERIALS AND METHODS
• How a programme project research results, treatment
performances or has performed including reasons for
expects of performance, whether positive or negative
which is usually with emphasis on impact for people,
research scholars, doctors, scientists, genetists,
paramedical staff and others

5. MATERIALS AND METHODS
• Protocol development
• Estimating the treatment difference in an individual
trial
• Combining estimates of a treatment difference across
the trials
• Meta-analysis using individual patient data
• Dealing with heterogeneity

6. RESULTS
• This article includes tries to describe new innovative
methods in research methodology. Effective monitoring is
important for purposeful conclusion of a research

7. SOFTWARE FOR META-ANALYSIS
• Comprehensive meta-analysis
• Epi meta
• Statistics software for meta-analysis
• Meta-analysis easy to answer
• Easy MA
• SAS 9.2 version and multiple regression models
• Stata version 9E/11.1

8. CONCLUSION
• Here the statistical analysis should be included in the
main protocol, although it may also be useful to
produce detailed statistical analysis plan separately
• For each outcome variable analysis of population
missing data at the subject level, analysis of individual
trials, meta-analysis model, estimation and hypothesis
testing are to be performed
• To avoid too many false positive results, it is desirable
to limit the number of covariables investigated in this
way

9. Thank You

10. CURRENT PROSPECTIVE IN GENE
THERAPY AND THE ROLE PLAYED BY
MEDICAL INFORMATICS
Mayadhar Barik, Doctorial Candidate, Department of Nuclear Medicine
All India Institute of Medical Sciences, New Delhi-110029
Email: mayadharbarik@gmail.com
Dr. Vaibhav Choudhary, Department of Medical Oncology
All India Institute of Medical Sciences, New Delhi-110029
Sushil K Meher, Department of Computer Facility
All India Institute of Medical Sciences, New Delhi-110029

11. INTRODUCTION
• Gene therapy aims to fix a disease linked with genetic
abnormality
• Gene therapy is currently receiving attention from the
scientists, clinicians and the general public in an attempt
to correct genetic defects including congenital
abnormalities and neurodevelopmental disorders.1
• However its application is beset with complications
partly because of lack of advanced knowledge and
research and partly because of vested interest by the
society.

12. OBJECTIVE
• This study is an attempt to highlight some of
the major benefits of gene therapy and to
define the role played by the medical
informatics in facilitating rational use of gene
therapy.

13. MATERIALS AND METHODS
• An attempt was made to review major studies in the
international literature to synthesize the potential risks,
benefits of gene therapy
• We also reviewed the tools and techniques in the field
of medical informatics which can be employed for
spreading awareness about gene therapy
• We developed a conceptual model of matching the key
concepts with the current protocols used in the field of
medical informatics.2

14. MATERIALS AND METHODS
• Chromosomes and Genes
• Chromosomal Disorders
• Genetic Disorders
• Inborn Errors or Metabolism (IEM)
• Prevention of Genetic Disorders (POGD)
• Common Inborn Errors of Metabolism (CIEOM)
• Gene or Gene Production
• Strategy for Gene Transfer
• Molecular Technique for Inducing the Gene
• Cell Type and Region

15. RESULTS
• Gene therapy offers a new hope for the treatment of
neurodegenerative disorders like Huntington disease. It
application in Parkinson disease has moved a step closer
to acceptance in the wake of its successful double blind
clinical trial
• The educational media, viz., fall under four categories,
audio media, the visual media, the audio-visual and the
virtual-media in vitro and vivo
• The simulation and animation capabilities of the media are
quite helpful in the conceptualization of gene therapy
• The audio-visual media are useful for counseling in matter
of genetic disorders the public.

16. DISCUSSION
• Many inherited metabolic diseases will not require
complete restoration of gene function for correcting
important aspects of the disease phenotype
• In e.g. Parkinsonism, phenylketouria and other
diseases. Pseudogene and transgenic expression is
needful.

17. DISCUSSION
• Age of the Affected Host
• Gene Therapy in Cancer
• Stem Cell and Gene Therapy in Molecular
Medicine
• Genetic Counselling
• Gene Therapy in Medical Informatics

18. CONCLUSION
• Our study suggests that an integrated and holistic use
of medical informatics can be highly beneficial for the
advanced application of gene therapy in the years to
come
• The current application of gene therapy has become
debatable and often controversial because of tendency
to use for manipulating the desirable attributes.3
• However its rational use is likely to benefit the
mankind.

19. REFERENCES
1. Osterman JV, Waddell A, Aposhian HV. DNA and gene therapy:
uncoating of polyoma pseudovirus in mouse embryo cells. Journal
Proceedings of the National Academy of Science of the United States
of America 67(1): 37-40.
2. Gennady E, Karl JH, Kevin TC, Sean S, Kelvin JAD. Regulators of
calcinurin (RCANIIL) is deficient in Huntington disease and protective
against mutant Huntington Toxicity in vitro. JBC Papers in Press.
Published on March 6, 2009 as manuscript M9006301200.
3. Samir C, Patela, Bhupendrarinh F, Chauhana KK, MM Patiala. Gene
therapy for neurodegenerative disorders: Current Status and Future
Prospects. Journal of Pharmacy Research Vol. 3, no. 5, 2010.
4. Friedmann T, Roblin R. Gene therapy for human genetic disease?
Science 1972; 175: 949.
5. Anderson WF. Prospects for human gene therapy. Science 1984; 226:
401.

20. REFERENCES
6. Verma IM. Gene Therapy, Sci Am 1990; 262: 68.
7. Gage FH, Fisher LJ, Jinnah HA, et al. Grafting genetically modified
cells to the brain: conceptual and technical issues. Prog Brain Res
1990; 82: 1.
8. Gage FH, Wolff JA, Rosenberg MB, et al. Grafting genetically
modified cells to brain: possibilities for the future. Neuroscience
1987; 23: 795.
9. Kobayashi T, Yamanaka T, Jacobs JM, et al. The twitcher mouse: an
enzymatically authentic model of human globoid cell leukodystrophy
(Krabbe disease). Brain Res 1980; 202: 479.
10. Igisu H, Suzuki K. Progressive accumulation of toxic metabolite in a
genetic leukodystrophy. Science 1984; 224: 753.

21. Thank You

22. Reducing EMR implementation cost and
improving adoption using eLearning for
training and change management.
Dr Saurabh Bhatia, MBBS(AFMC), MS, FCR
Managing Director, TSML Solutions Pvt Ltd, Pune
S.Bhatia@TSMLS.org;
!1

23. Introduction
• Going live after a
successful
implementation of an
HIS or EMR in a big
hospital is the
beginning of the
problem for the
clinical adoption,
training and change
management team.
!2

24. This case study uses
personal experience
• Two quaternary care hospitals of
Delhi.
• One greenfield, one functional
hospital
• Bed strength was around 900
then, planned to shoot upto 1600
in 2-3 years
• Workforce was Huge
• EMR was first time introduced at
both hospitals
• Clinical adoption was suspect,
even before implementation
!3

25. The numbers
• The number of personnel to be trained in each hospital
were very similar
• 2400 nurses
• 300 in house doctors
• 600 visiting doctors (brownfield hosp)
• additional staff of about 600 employees comprising of
pharmacy, store keepers, front-office, quality department,
secretaries and clerical staff.
• The total people to be trained were estimated to be
about 4000.
!4

26. Training logistics
• After preparing the training material and
running a short pilot on a mixed group,
we came to the conclusion that about
• 8 hours of training was required by
every individual.
• The traditional method to train is to
make a set of about 20 people sit in
a computer lab, with 2 instructors
who will train them.
• Further, the training was to be done in
4 periods of 2 hours each, because
it was expected that new learners
cannot focus at a stretch for 8 hours to
learn effectively.
!5

27. Problem Statement …1/4
• Different set of employees needed different
combination of module trainings. It was thus
difficult to merely create a mathematical group of 20
trainees, but needed a group of 20 trainees with
similar needs.
• Batch making was further complicated by different
shift of duties of various personnel falling in the
same batch.
!6

28. Problem Statement …2/4
• The 2400 nurses who needed training could not be
spared during duty hours. So they would have to be
allocated before or after duty hours. This meant
abnormal timings for training.
• In greenfield hospital, nurses were being hired at a very
rapid pace and HR was unable to give us exact numbers
and training plans
• All doctors will not be able to attend as scheduled
because of emergency and OT schedules and batches
of 20 were seen impossible most of the time.
!7

29. Problem Statement …3/4
• Visiting doctors visit once or twice in a week and it
was impossible to force them to attend pre-
planned training sessions.
• Both hospitals had only one computer lab with 6-8
computers but were willing to upgrade it to 10
computers. But another 10 computer lab had to be
raised or outsourced, perhaps at a different location.
!8

30. Problem Statement …4/4
• The nurses had an attrition rate of about 25% which meant
that about 600 new nurses will have to be trained every year
subsequently, practically meaning about 2-3 new nurse
trainees will trickle in every day.
• For the remaining force, the attrition was estimated at 8% which
meant about 125 new trainees every year.
• The total training was for 200 batches at a conservative
estimate, and was expected to spill over 250 batches,
utilizing 16-20 students, of 8 hours each, totaling 2000 hrs of
class room training.
• This meant 250 working days of training which meant 10-11
months to train the entire workforce, if trainings were
conducted for 8 hrs everyday, 25 days a month.
!9

31. Problem compounding
• Time-Frame was unacceptable
• Timeframe alone could defeat the adoption programme
• Cost of training was too High
• Cost of training Nurses: INR 18,00,000 (600/day x 3000)
• Cost of training doctors: INR 18,00,000 (2000/day x 900)
• Cost of training others: INR 4,00,000 (650/day x 600)
• Cost of trainers: INR 8,00,000 (2000 hrs x 200/hr)
• Additional Infra: computers, Labs etc: 5,80,000
• Total cost: 52.8 lakhs
!10

32. What we were expected to do
• Finish training in a reasonable time frame, commensurate
with implementation plan
• Bring the cost down as a training worth ½ crore was not
affordable
• Ensure that employees, whether onboarded or old, were all
able to make/ keep hospital functional
!11

33. Our Approach
• Mixed Approach
• Synchronous+Asynchronous training
• We implemented an Enterprise Training Portal (ETP)
which behaves like a website. It was possible to access
this training material from within hospital premises, and
if permitted, from home also.
• All the training material was uploaded in ETP.
• Role based courses were created for each role e.g. Nurse,
doctor, accounts etc.
• Each user was asked to self-register and self-enroll in
courses.
• Each course had training material, practice sessions, mock
and final tests
• Each candidate was awarded a certificate of passing the
modules that their role demanded.
!12

34. Our Approach
• Synchronous:
• It was decided that classroom training would be given to each
person for not more than 2 hours.
• First 30 minutes to teach how to utilize eLearning for training.
Next 90 minutes, split over 2 sessions for doubt clearing and
advanced questions.
• The second session was a free for all doubt clearing session,
not mandatory.
• Strategically identified key people, which were more or
less one person per department, were trained for a longer
duration to become training champions
!13

35. Our Approach
• Nurses and doctors were each given a timeline
of 45 days from enrollment to complete their
training using bits and pieces of free time that they
get in the day or night duty.
• This was made a mandatory part of all new
joinees' induction training. (remember 750 new
employees?)
• The users were not allowed on the main EMR till
they had not obtained the certificate from LMS.(Part
success)
!14

36. Changes that happened
• Changes in the time taken for training
• 2 months, a critical mass was reached to call it an
adoption
• Changes in the cost of training
• The need for formal classroom training was brought down to one fourth the
number of hours with just 30% computers.+ward PCs+PCs
• The actual expenses that occurred due to this different strategy are as follows:
• Cost of nurses training: 4.5 lakhs (¼ of traditional model)
• Cost of doctors' training : 4.5 lakhs (¼ of traditional model)
• Cost of training other personnel: 1.5 lakhs (2/5th of traditional model)
• Cost of trainers: 2 lakhs ((¼ of traditional model)
• Cost of additional computers: Nil
• Additional cost of web-material building: Nothing extra was built. We used
existing Powerpoint or Word files.
• Additional expense for LMS configuration: 2 lakhs
• Total training expense: about 14.5 lakhs
!15

37. How green was my
valley?
• How succesful were we in adoption?
• Usage started across the hospital
• 65-75% employees were using the systems
• Patterns Noted:
• Nurses learnt with max enthusiasm
• Their champions also had better inputs
• Doctors’s enthusiasm curve declined with seniority
• Secretaries had to be trained instead of HsOD
• Laboratories were more critical of changed processes but
learnt the systems faster.
• Accounts and admin dept were resistant to change
• Front office had a mixed response
• Medical coding was not a part of this exercise
• External doctors were casual, at best. Most were not given
login
!16

38. Thanks for your patience.
You were listening to a
presentation by
Dr Saurabh Bhatia, MBBS(AFMC), MS, FCR
Managing Director,
TSML Solutions Pvt Ltd, Pune
S.Bhatia@TSMLS.org
About
Reducing EMR implementation cost and
improving adoption using eLearning for
training and change management.
!17