“8th National Biennial Conference on Medical Informatics 2012”
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: email@example.com Sushil K Meher, Department of Computer Facility All India Institute of Medical Sciences, New Delhi-110029
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
OBJECTIVE• Its efficiency, effectiveness, sustainability and reproducibility and hence play a crucial role in medical research and education
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
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
RESULTS• This article includes tries to describe new innovative methods in research methodology. Effective monitoring is important for purposeful conclusion of a research
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
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
CURRENT PROSPECTIVE IN GENETHERAPY 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: firstname.lastname@example.org 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
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.
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.
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
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
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.
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.
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
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.
REFERENCES1. 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.
REFERENCES6. 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.
Reducing EMR implementation cost andimproving 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
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
This case study usespersonal 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 AboutReducing EMR implementation cost andimproving adoption using eLearning for training and change management. !17