IT in clinical research


Published on

Published in: Business, Technology
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

IT in clinical research

  1. 1. Lecture series on Clinical Research # Title Date 1 What research? And what CRC does Done 2 Developing research capacity Done 3 •Governance & Ethics Done 4 •Methodology & protocol development Done 5 •IT in Clinical research 6 •Biostatistics 7 •Medical writing & publication 8 Develop data resources 9 •Patient registries 10 •Healthcare Surveys 11 Investigator initiated research 12 Contract Research industry
  2. 2. Dr. Lim Teck Onn FRCP, M.Stat Information Technology in Clinical Research
  3. 3. Research capacity & competencies in CRC # Research competencies Sources 1 Governance, Ethics and Compliance CRC’s core 2 Subject matter expertise MOH & CRC 3 Methodology & protocol development CRC’s core 4 Project mgt & Research QA CRC’s core 5 Information Technology Vendors 6 Data management Partner 7 Biostatistics Partner 8 Medical writing, Publication & publishing Partner 9 Specialized functions: Clinical Lab, Safety, Logistics, etc Vendors
  4. 4. Quotes IT and data are for clinical research like what labs, animal house & tissue repository are for basic research; they are mission-critical resources The data collected by a clinical research is its justification and purpose. No matter how perfect the protocol or experienced its investigators, if data cannot be reliably and accurately collected and stored for analysis, the research might as well not have been performed in the first place
  5. 5. Contents Uses of IT in research Research data collection: “eClinical” Data warehousing Information security
  6. 6. Uses of IT in clinical research Common IT tools supporting various research processes # Research process Enabling IT tools 1 Conception & lit review Internet, Medline 2 Research design Sampling, Randomization, Sample size etc 3 Research conduct Research system; “eClinical”, Data warehousing 4 Statistics SAS, STATA 5 Medical writing Endnote 6 Publication Desktop publishing, Manuscript Central 7 Dissemination PLoS medicine (open access journal) CRC website, Public use dataset (Data download), Data charting tool 8 Research governance & compliance Research registration, NMRR (electronic research submission, processing & performance tracking)
  7. 7. Research data collection Collecting and managing data is a fundamental activity in research, there are basically 2 ways to go about it 1. Primary data collection for research: eClinical Collect data per protocol designed to meet research objective Traditional paper CRF IVRS/ Call centre Electronic data capture (EDC): web-based application 2. Secondary uses of existing health databases: Data warehousing Issues with data: Unstructured or non-standardized; Duplicates; Often require data in multiple databases; Data distortions (missing value, error)
  8. 8. Difference bet. Research and Routine health data Research data eg Trial Routine data eg Hospital, Lab etc 1 Data per protocol Multi-purpose 2 Dedicated system; usually off the shelf Huge complex system, often bespoke 3 Use one-off per project Ongoing concern 4 System validation required Costly luxury 5 Data QA built-in Costly luxury 6 Data professionally managed (MCSE, CCDM, Stats etc) Costly luxury 7 Anonymized data Security concern ++ 8 Cost++ Cost++ Must deliver high quality data, else you are fired Data for patient care; must deal with inherent limitation for research use
  9. 9. Contents Uses of IT in research Managing research data collection: “eClinical” Accessing & processing routine health data for research: Data warehousing Information security
  10. 10. eClinical The data collected by a clinical research is its justification and purpose. Not surprisingly, IT plays a key role in enhancing the research conduct # Research mgt Research systems 1 Project mgt Clinical Trial Management Systems (CTMS) eInformed Consent Patient recruitment 2 Data collection & mgt eCRF or Electronic Data Capture (EDC) Electronic diaries, Lab & Imaging data Clinical Data Mgt system (CDMS) 3 Logistics mgt Randomisation and Trial Supply Management (RTMS)
  11. 11. CDMS Clinical Data Management System (CDMS) is a comprehensive research system to collect, manage, and review trial data Key Features Data Entry Data Coding Data Validation Data Retrieval
  12. 12. ClinDataTrack Capture, process and track the CRF and Query form CRF and Query scanned as images for simultaneous processing and access Key features: Reduce paper work Reduce the time and cost Reduce risk of document loss CDM How many CRF have Logged-in? Has the site return the Query form? What is the turn around time for query? How many CRF have not yet scanned?
  13. 13. ClinDataTrack Generate and Track Data Clarification Form (DCF) -Track and Scan CRF - Image Review
  14. 14. Central Randomization System A system that generates the random allocation of treatment to successive patients within a clinical trial. Often done centrally to prevent randomization being subverted IVR and web-based technology provides real-time 24 x 7 randomisation Linked to Clinical Data Management System (CDMS) and Clinical Supply system
  15. 15. IVR Central Randomisation IVRS Study Site Sponso r Monitor Clinical Supply CDMS
  16. 16. Clinical Supply System A system to plan, monitor and control the entire supply chain process in clinical trial Ensure all investigational product can be accounted for at the end of the study IVR and web-based technology optimizes supply chain process and provide robust inventory control
  17. 17. IVR Clinical Supply System IVR Inventory Database Drug Distribution Depot Study Site 1. Dispense Call 2. Pack Number 3. Stock levels fall to trigger level 4. Consignment Request 5. Consignment Details 6. Shipment 7. Notification call: Arrival/ damaged packs 8. Update list of available packsTelephone Communication via IVR Automated electronic communication Physical Shipment
  18. 18. Safety Reporting System A system to capture, store, process, maintain, classify and report adverse event data. Key Features Data Entry Data Coding Data Review Data Evaluation Report (Expedited Reporting, PSUR)
  19. 19. Data Entry Data Coding EvaluationReporting
  20. 20. Web Application for Registry & Healthcare survey Registry operations day-to-day : SDP mgt, tracking patient notification, query mgt eCRF Electronic data capture (EDC) tool Online data access To enable source data providers (SDP) access & manage their listed patients To enable other authorized non-SDP users to gain access to data for other purposes (transplant waiting list, research purpose, on-demand data charting etc)
  21. 21. Web applications for CRC projects # Patient registries Health databases 1 National Renal Registry National Suicide Registry M’sian Cardio- Thoracic Registry Healthcare Establishment & Workforce Survey 2 National Transplant Registry Nat. CVD (ACS/ PCI) Database National Neurology Registry Health professional registers 3 National Eye Database Nat. Dermatology Registry National Chest Registry National Medicines Use Survey 4 Malaysian National Neonatal Registry National Cancer Patient Registry National Urology Registry National Medical Device Survey 5 Malaysian Liver Registry Hematological Malignancy Reg. National ORL Registry National Medical care Survey 6 Malaysian GI Registry National OT Register National Nuclear Medicine Database JPN National Death Register 7 National Trauma Database Malaysian Registry of Intensive Care National Radiology Registry Post-Operative Mortality Review 8 Diabetes Registry of Malaysia Nat. Inflammatory Arthritis Registry National O&G Patient Registry Maternal Mortality Register 9 National Mental Health Registry Nat. Orthopedic Reg Malaysia National Paediatric Mortality Register
  22. 22. IT infrastructure supporting CRC projects Coordinating centre NTT Data Centre, Cyberjaya Connectivity between coordinating center and data centre conducted viaA Virtual Private Network (VPN) over broadband internet connection.
  23. 23. eClinical: Towards convergence As more research activities are conducted and data collected using e- methods; the piecemeal approach in the past is converging to provide a seamless flow in order to save time, streamline workflow and improve accuracy Combining solutions and access data & all functionalities through a single interface Business-process driven solutions; focus on workflow and how to simplify that for users using multiple technology solutions to do their jobs in a single project Integration rather than homegenization: Better to combine the best solutions rather than attempt to rebuild a single new monolithic system that does everything but will likely have limitations in key areas.
  24. 24. Computer System Validation (CSV) “… an ongoing process of establishing documented evidence which provides a high degree of assurance that a computerized system will consistently perform according to its predetermined specifications and quality attributes”
  25. 25. Lifetime system validation goals Management control Controlled GCP work processes using computerized systems System reliability Consistent, intended performance of computerised systems Data integrity Secure, accurate, and attributable GCP e-data Auditable quality Documented evidence for control and quality of e-data and e-system e
  26. 26. Validation plan Application administrative SOPs & application configuration management logs Change control log, QA audit log, supplier reports & BDG minutes Needs analysis, RFP, contract, URS, SLAs User manuals, CVs & training records, dept. SOPs, problem/help logs Test summary report & updates Test cases, scripts, data & results logs Test plan(s) start-up & ongoing Typical CSV package items Users’ CSV package summary report
  27. 27. Contents Uses of IT in research Managing research data collection: “eClinical” Accessing & processing routine health data for research: Data warehousing Information security
  28. 28. Information needs for our healthcare Resource Inputs Care Process Service Outputs Population Health Outcomes •Financing •Workforce •Facilities •Drugs •Medical technology/ Devices •Services •Diagnosis •Therapy •Rehabili- tation •Quality of care •In-patient care •Ambulatory care •Surgical Procedures •Clinical measures: BP, Lipid etc •Mortality •QOL •Rehabilita- tion Population illness burden •Disease incidence & prevalence •Environ- ment Healthcare System
  29. 29. Where are the data? # Data domain Data sources 1 Illness burden Population health survey, Routine health service, Epidemiology research, Disease surveillance, Patient registries 2 Financing National Health Account; Health Econ research 3 Facilities & Services NHSI 4 Human resource Professional register; NHSI 5 Medicines NMUS, EMR (THIS, TPC) 6 Medical Technology NMDS 7 Patient Rx Patient registries, EMR (THIS, TPC) 8 Healthcare quality Patient registries, EMR (THIS, TPC), Quality of Care Indicators, Incident reporting 9 Healthcare activities (in-patient, ambulatory care, surgery) NHSI, NOTRE, Routine health service, EMR (THIS, TPC) 10 Health outcomes Patient registries, Vital registration; Health Outcome research
  30. 30. Data warehousing A data warehouse is a repository of an organization's electronically stored data; it is designed to facilitate analysis and reporting Health Data sources •Hospital CIS •TPC •Lab IS •Hospital discharges •Surgical records •Admin/Billing systems •Procurement (eg medicine, devices) •Asset inventory •Population health surveys •Healthcare surveys •Patient registries Data warehousing & processing •Encoding algorithm •Data de-duplication •Record linkage •Data cleaning (missing data & rectifying other data distortion) Values •Public use data •On demand data charting •Monitoring & Evaluation (M&E) •Routine statistical report •Research use
  31. 31. What information value? The problem is how best to learn from the data that is captured in our health databases? How to extract any potentially useful information from the available data to inform decision-making, solve problem or simply to discover new knowledge (research).
  32. 32. Terminology Data Raw facts generally stored as characters, words, symbols or measurements Information Processed data. By processing is meant anything done to the raw data from formal analysis to explanations supplied by the user. Knowledge Information applied to rules, experiences and relationships, with results that it can be used for decision making or problem solving. Data mining The science of searching large body of data seeking interesting and unsuspected patterns and structures Research Systematic investigation (usually based on data) to obtain generalizable new knowledge
  33. 33. From data to information to knowledge Data Information Knowledge Dataprocessing Is our massive investment in healthcare IT producing the desired information value?
  34. 34. How is it done? How to extract useful information from available health databases? • Who: Who should be doing what? • How: Getting from data to information?
  35. 35. Who does what? Elliot. “Where is the knowledge in the information? And where is the wisdom in the knowledge?” Message: So, don’t leave it to the IT professional alone Michael Healy, medical statistician; on being ask about poor quality medical research. “The difference between medical and agricultural research is that medical research is done by doctors but agricultural research is not done by farmers.” Message: Neither can you leave it to doctors Stalin, Russian dictator. “There are lies, great lies and statistics” Message: So, you cannot trust the statisticians either
  36. 36. It takes many different skills 1 ICT pro Data How best to employ available technology to manage & secure the data captured in the healthcare system? 2 Data mgt Data How best to extract the required data, and then to “clean” and process the data to enable the generation of information from the data? 3 Stat Stats How best to apply available statistical methods to estimate parameters of interest (information expressed as statistics)? 4 Doctor Text + # How best to interpret and act on the available data and information to manage my patients? 5 Manager Report + stat How best to interpret and act on the available information to manage (financial, HR, operational etc) the healthcare organization? 6 Researcher Stats How best to interpret the statistical information, and obtain generalizable
  37. 37. Getting from data to information 1. Data processing Encoding algorithm Data de-duplication Record linkage Data cleaning & editing 2. Data analysis Statistical methods Data mining Data Information Knowledge Dataprocessing
  38. 38. De-duplication Process of finding records that refer to the same entity in one table De-duplication methods: String comparison Phonemic name comparison Non phonetic fuzzy matching Linguistic name analysis Specialized numeric comparisons such as distance comparison, date/time comparison User defined rules
  39. 39. Coding “In order to make coded data available in a setting where a large subset of the information will reside in natural language documents, a technology called natural language understanding is required. This technology allows a computer system to “read” free-text documents, to convert the language in these documents to predefined concepts and to capture these concepts in a coded form in a medical database” By Peter J. Haug
  40. 40. Auto Coding Multiple Data Dictionary ICD-10, ICD-O (Oncology) MedDRA FDA COSTART WHO WHOART Anatomic Therapeutic Classification Autocoding methods: Parsing Machine learning Error analysis Decision making Probabilistic matching of text against coded data (ICD-O) Data mining
  41. 41. Eg. Specimen: Left breast & axillary lymph node Diagnostic basis: Infiltrating ductal carcinoma (NOS) grade 2 Software coding generates: 7 - [BREAST] [DUCTAL] => C50.9 AutocodingAutocoding
  42. 42. Coding Adverse Event Text description of reported AE Acute myocardial infarction Increase in blood pressure Blood pressure increased Fatigue Blood pressure increase (hypertension) MedDRA Preferred Term (PT) code + label 10000891 Acute myocardial infarction 10005750 Blood pressure increased 10005750 Blood pressure increased 10016256 Fatigue 10020782 Hypertension NOS
  43. 43. Record Linkage Task of linking together information from one or more data sources that represents the same entity. This technique is used to determine whether two records represent the same real-world entity. (Peter Christen, Tim Churches, 2000) Uses similarity-search technique in order to search for similar records (e.g misspelt character in a name)where it is able to determine only those that are actual duplicates.
  44. 44. Record Matching with NVRS dataRecord Matching with JPN data
  45. 45. Data management Process of detecting, diagnosing, and editing faulty data (missing, disallowed, inconsistent, out of the range, etc.) Data editing: Correction of the data shown to be incorrect
  46. 46. Data analysis & reporting 1. Statistical methods Refer presentation on Biostatistics 2. Data mining The science of searching large body of data seeking interesting and unsuspected patterns and structures Any computer method of automatic and continuous analysis of data, which turns it into useful information (Edwards, Data mining 2002)
  47. 47. The Collaborative Research experience Information Technology professionals Users (managers & doctors) Research Organization eg CRC DIY by ICT or manager/ doctor should be history Collaborative Research Experience
  48. 48. Contents Uses of IT in research “eClinical” Data warehousing Information security
  49. 49. Privacy & Confidentiality considerations Definitions 1. Privacy: An individual’s right to control identifiable health information (in healthcare or research context) 2. Confidentiality: Confidentiality is the corresponding duty to protect privacy right. It comprises those legal or ethical duties that varies in specific relationship, such as between doctor & patient; investigator and subject; custodian & donor 3. Security: This refers to the technological and administrative safeguards or tools to protect identifiable health information from unwarranted access or disclosure
  50. 50. What research guideline says… Declaration of Helsinki 2008 Paragraph 11 & 23 “It is the duty of physicians who participate in medical research to protect the life, health, dignity, integrity, right to self-determination, privacy and confidentiality of personal information of research subjects” “ Every precaution must be taken to protect the privacy of research subjects and the confidentiality of their personal information and to minimize the impact of the study on their physical, mental and social integrity”
  51. 51. And what the law says.. “The law in all countries assumes that whenever people give personal information to health professionals caring for them, it is confidential as long as it remains personally identifiable” Malaysia Medical Act 1971. Data Protection Act 2009 US has the most comprehensive set of regulations, and even then they are incomplete and inconsistent Common Rule 45 CFR 46 Protection of human subjects Health Insurance Portability and Accountability Act (HIPAA) 1996. 45 CFR 160/164 Standards for Privacy of Individually Identifiable Health Information
  52. 52. Friday, January 24 2003   BY LEE KAR YEAN in Kajang  THE government is prepared  to enact a privacy and data  protection law to address the  growing concern among  Internet users about invasion  of their privacy via the Web,  Second Finance Minister  Datuk Jamaludin Jarjis said.    He  said  the  government  was  drafting  a  bill  for  deliberation  by  parliament  in  the  wake  of  concerns  raised  by  consumers  and  in  the  interest of building confidence  in the electronic market place. “Any  privacy  and  data  protection law enacted cannot  be perfect but we have several  models that we can  look  at  and  improve  upon.  We  need  to  provide  some  flexibility  for  adjustments  within online services in line  with  changes  in  technology,”  Jamaludin  said.    His  remarks  were  contained in a speech read  out on his behalf by Deputy  Finance  Minister  Datuk  Chan  Kong  Choy  at  the  conclusion of a seminar on  E-Commerce & the Law on  Privacy and the launch of a  book  titled  Privacy  &  Data  Protection  at  Universiti  Tenaga  Nasional  in Kajang  yesterday.  Jamaluddin  said  the  government, in consultation  with  the  public  and  private  sectors,  had  come  up  with  a comprehensive  personal  data  protection  law.  The  legislation  would  provide  the  mechanism  for  collecting,  processing  and  using  data  held by the public and private  sectors.    “It  provides  the  individual  a  remedy  in  case  of  misuse  of  data.  It  seeks  to  protect  the individual from unwanted  or  harmful  use  of  their  data.  As  such,  the  data  privacy  regime  in  Malaysia  does  not  seek  to  cut  off  the  flow  of  data  but  merely  to  see  that  they  are  collected  and  used  in  a  responsible  and  accountable  manner,”  he  added.  Jamaluddin  said  that  although  the  Internet  provided  conveniences  from  e-mail to online shopping and  access  to  share  market  quotes,  the  potential  abuses  and invasion  of  privacy  via  the  Internet were innumerable.    “What  is  less  understood  is  that  the  Internet  also  collects  a  great  deal  of  information  about  its  users.  As  to  who  uses  this  information  and  how  it  is  used  forms  the  basis  of  many  Internet  privacy  concerns,” he said.    Jamaluddin  also  noted  that  different  countries  had  adopted  different  legal  methods  and  instruments  to  combat  the  invasion  of  privacy  issue,  with  the  European  Union  using  the  regulatory  approach  and  the  US  adopting  the  self- regulatory one. Govt ready to enact  privacy and data  protection law
  53. 53. And what the technical guideline says.. The ethical and legal requirements to protect P&C naturally give rise to considerations of security; the technological & admin safeguards in place in healthcare institution or research organization to protect identifiable health information from unwarranted access or disclosure” Technical standards • ISO/IEC17799:2002 • Malaysian Public  sector management of  ICT Security  handbook. MAMPU  2001
  54. 54. Information Security in research org. Are you having sleepless nights over this? Every research database manager’s (and sponsor’s) nightmare What it takes? Security policy & procedures Staff awareness and training Security audit & ISO to ensure compliance Technology
  55. 55. Technological Mechanisms to ensure Security Authentication Access control Encryption Audit trail Physical security Control of external communication links and access System backup and disaster recovery An SMS containing additional password is sent to user’s mobile phone
  56. 56. Thank You