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Health IT in Hospital Settings

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A presentation on Dec. 3, 2010 for Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand.

A presentation on Dec. 3, 2010 for Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand.

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  • 1. Health IT in Hospital Settings Nawanan Theera-Ampornpunt MD MSNawanan Theera Ampornpunt, MD, MS Healthcare CIO Program, Ramathibodi Hospital Administration School Dec. 3, 2010 SlideShare.net/Nawanan Except where  citing other works
  • 2. Health Care SystemHealth Care System Home Hospital Emergency Responders N i H /HospitalResponders Nursing Home/ Long-Term Care Facility Ministry ofy Public Health The Payers Clinic/ Physician’s Office Community Lab Pharmacy Health Center (PCU)Lab
  • 3. Hospital’s RolesHospital s Roles • Provider of Secondary & Tertiary CareProvider of Secondary & Tertiary Care – Acute Care – Chronic Care – EmergencyEmergency • Facilitator of Primary Care • Sometimes Teaching & Research
  • 4. Levels of HospitalsLevels of Hospitals • Community HospitalsCommunity Hospitals • General/Provincial Hospitals • Tertiary/Regional Hospitals U i i M di l C• University Medical Centers • Specialty HospitalsSpecialty Hospitals
  • 5. Types of HospitalsTypes of Hospitals • PublicPublic • Private For-Profit • Private Not-For-Profit • Stand-AloneStand Alone • Part of Multi-Hospital System
  • 6. Why They Matter: The Importance of “Context”Why They Matter: The Importance of Context • $$$ (Purchasing Power)$$$ (Purchasing Power) • Bureaucracies & regulations • Organizational cultures & management styles L l f i i l/ kfl l i• Level of organizational/workflow complexity • Facilities & level of information needsFacilities & level of information needs • Service volume, resources, priorities • Internal IT capabilities & environments
  • 7. IT Decision Making in Hospitals: Key PointsIT Decision Making in Hospitals: Key Points • Depends on local contextDepends on local context • IT is not alone -> Business-IT alignment/integration • “Know your organization” Vi IT l f hi l h• View IT as a tool for something else, not the end goal by itself • Focus on the real goals (what define “success”)
  • 8. Success of IT ImplementationSuccess of IT Implementation DeLone & McLean (1992)DeLone & McLean (1992)
  • 9. CLASS EXERCISE #3CLASS EXERCISE #3 Suggest 2-3 examples of “success”Suggest 2 3 examples of success of IT implementation in hospitals f h f D L & M L ’for each of DeLone & McLean’s Model (1992)( )
  • 10. Success of IT ImplementationSuccess of IT Implementation System QualitySystem Quality • System performance (response time, reliability) • Accuracy, error rate Fl ibili• Flexibility • Ease of useEase of use • Accessibility
  • 11. Success of IT ImplementationSuccess of IT Implementation Information QualityInformation Quality • Accuracy • Currency, timeliness R li bili• Reliability • CompletenessCompleteness • Relevance • Usefulness
  • 12. Success of IT ImplementationSuccess of IT Implementation UseUse • Subjective (e.g. asks a user “How often do you use the system?”)system? ) • Objective (e.g. number of orders done electronically) User Satisfaction S i f i d /i f i• Satisfaction toward system/information • Satisfaction toward useSatisfaction toward use
  • 13. Success of IT ImplementationSuccess of IT Implementation Individual ImpactsIndividual Impacts • Efficiency/productivity of the user • Quality of clinical operations/decision-making Organizational ImpactsOrganizational Impacts • Faster operations, cost & time savings • Better quality of care, better aggregate outcomes • Reputation increased market shareReputation, increased market share • Increased service volume or patient retention
  • 14. NOW WHAT ARE SOMENOW, WHAT ARE SOME IMPORTANT HOSPITAL IT?
  • 15. Examples of Hospital ITExamples of Hospital IT Enterprise-widete p se de • Infrastructural IT (e.g. hardware, OS, network, web, e-mail) Office Automation• Office Automation • MPI, ADT • EHRs/EMRs/HIS/CIS • CPOE & CDSSs • Nursing applications • Billing Claims & Reimbursements• Billing, Claims & Reimbursements • MIS, ERP, CRM, DW, BI
  • 16. Examples of Hospital ITExamples of Hospital IT Departmental ApplicationsDepartmental Applications • Pharmacy applications • LIS, PACS, RIS • Specialized applications (ER OR LR AnesthesiaSpecialized applications (ER, OR, LR, Anesthesia, Critical Care, Dietary Services, Blood Bank) I id t t & ti t• Incident management & reporting system • E-Learning • Clinical research informatics
  • 17. 4 Quadrants of Hospital IT • Business CDSS Modified from Strategic Business Intelligence • MIS • Data Mining/ Utilization • CDSS • HIE • CPOE • PACS • Medication Theera- Ampornpunt, 2010 Utilization • Customer- Relationship Management • Medication Safety Apps • EHRs Cli i lAd i i t ti • Enterprise Resource Planning • HIS • RIS LIS ClinicalAdministrative Planning (Finance, Materials, HR) • Data Warehouse • Office • LIS • ADT • MPI • Most d t t l• Office departmental systems OperationalPosition may vary based on local context OperationalPosition may vary based on local context
  • 18. The IT InfrastructureThe IT Infrastructure
  • 19. Infrastructural ITInfrastructural IT • HW/SW Acquisition installation & maintenanceHW/SW Acquisition, installation & maintenance • System administrationadministration • Network d i i t tiadministration • Security
  • 20. Infrastructural ITInfrastructural IT Issues • Expertise • Insourcing vs. Outsourcing • Policy & Process Controls • Best Practices in Design & Management • Documentation!!! • Risks – Confidentiality/Integrity – Outages R d d C t– Redundancy vs. Cost – Configuration complexities & patch management – Compatibility & Technology ChoicesCompatibility & Technology Choices
  • 21. The Clinical ITThe Clinical IT
  • 22. Master Patient Index (MPI)Master Patient Index (MPI) • A hospital’s list of all patients • Functions – Registration/identification of patients (HN/MRN) – Captures/updates patient demographics – Used in virtually all other hospital service applicationsy p pp • Issues – A large databaseA large database – Interface with other systems – Duplicate resolutionsDuplicate resolutions – Accuracy & currency of patient information – Language issues– Language issues
  • 23. Admit-Discharge-Transfer (ADT)Admit Discharge Transfer (ADT) • Functions S t Ad it Di h & T f f ti t– Supports Admit, Discharge & Transfer of patients (“patient management”) – Provides status/location of admitted patientsProvides status/location of admitted patients – Used in assessing bed occupancy – Linked to billing, claims & reimbursementsg, • Issues – Accuracy & currency of patient status/location – Handling of exceptions (e.g. patient overflows, escaped patients, home leaves, discharged but not yet departed, missing discharge information)missing discharge information) – Input of important information (diagnoses, D/C summary) Links between OPD IPD ER & OR– Links between OPD, IPD, ER & OR
  • 24. EHRs & HISEHRs & HIS The Challenge - Knowing What It Means Electronic Health Records (EHRs) Hospital Electronic Medical Records (EMRs) Hospital Information System (HIS) Records (EMRs) Electronic Patient C t B d Records (EPRs) Personal Health Clinical Information System (CIS)Computer-Based Patient Records (CPRs) Records (PHRs) System (CIS)
  • 25. EHRsEHRs Commonly Accepted Definitions El t i d t ti f ti t b id• Electronic documentation of patient care by providers • Provider has direct control of information in EHRs • Synonymous with EMRs EPRs CPRs• Synonymous with EMRs, EPRs, CPRs • Sometimes defined as a patient’s longitudinal records over several “episodes of care” & “encounters” (visits)p ( )
  • 26. EHR SystemsEHR Systems Are they just a system that allows electronic documentation of clinical care?clinical care? DiHi TDiag- nosis History & PE Treat- ments ... Or do they have other values?
  • 27. Documented Benefits of Health ITDocumented Benefits of Health IT • Literature suggests improvement through – Guideline adherence (Shiffman et al, 1999;Chaudhry et al, 2006) – Better documentation (Shiffman et al, 1999) – Practitioner decision making or process of care (Balas et al, 1996;Kaushal et al, 2003;Garg et al, 2005) Medication safety– Medication safety (Kaushal et al, 2003;Chaudhry et al, 2006;van Rosse et al, 2009) – Patient surveillance & monitoring (Chaudhry et al, 2006)g ( y ) – Patient education/reminder (Balas et al, 1996) – Cost savings and better financial performanceg p (Parente & Dunbar, 2001;Chaudhry et al, 2006;Amarasingham et al, 2009; Borzekowski, 2009)
  • 28. Functions that Should Be Part of EHR SystemsFunctions that Should Be Part of EHR Systems • Computerized Medication Order Entry (IOM, 2003; Blumenthal et al, 2006) • Computerized Laboratory Order Entry (IOM, 2003) • Computerized Laboratory Results (IOM, 2003) • Physician Notes (IOM, 2003) • Patient Demographics (Blumenthal et al, 2006) • Problem Lists (Blumenthal et al, 2006) • Medication Lists (Blumenthal et al, 2006)Medication Lists (Blumenthal et al, 2006) • Discharge Summaries (Blumenthal et al, 2006) • Diagnostic Test Results (Blumenthal et al 2006)• Diagnostic Test Results (Blumenthal et al, 2006) • Radiologic Reports (Blumenthal et al, 2006)
  • 29. EHR Systems/HIS: Issues • Functionality & workflow considerations • Structure & format of data entryStructure & format of data entry – Free text vs structured data forms – Usability – Use of standards & vocabularies (e.g. ICD-10, SNOMED CT) – Templates (e.g. standard narratives, order sets) – Level of customization per hospital, specialty, location, group, clinician – Reduced clinical value due to over-documentation (e.g. medico-legal, HA) Special documents (e g operative notes anesthetic notes)– Special documents (e.g. operative notes, anesthetic notes) – Integration with paper systems (e.g. scanned MRs, legal documents) • Reliability & contingency/business continuity planningReliability & contingency/business continuity planning • Roll-out strategies & change management • InterfacesInterfaces
  • 30. Computerized (Physician/Provider) Order Entry Functions Ph i i di tl t di ti /l b/di ti /i i• Physician directly enters medication/lab/diagnostic/imaging orders online • Nurse & pharmacy process orders accordinglyNurse & pharmacy process orders accordingly • Maybe considered part of an EHR/HIS system Values • No handwriting!!! • Structured data entry (completeness, clarity, fewer mistakes) • No transcription! • Entry point for CDSSs • Streamlines workflow, increases efficiency
  • 31. Computerized (Physician/Provider) Order Entry Issues “Ph i i l k” f t ti• “Physician as a clerk” frustration • Usability -> Reduced physician productivity? • Unclear value proposition for physician?• Unclear value proposition for physician? • Complexity of medication data structure • Integration of medication lab diagnostic imaging &other ordersIntegration of medication, lab, diagnostic, imaging &other orders • Roll-out strategies & change management Washington Post (March 21, 2005) “One of the most important lessons learned to date is that the complexity f h h b il d i d”of human change management may be easily underestimated” Langberg ML (2003) in “Challenges to implementing CPOE: a case study of a work in progress at Cedars-Sinai”
  • 32. Clinical Decision Support Systems (CDSSs) • The real place where most of the values of health IT can be achievedachieved • A variety of forms and nature of CDSSs – Expert systemsp y • Based on artificial intelligence, machine learning, rules, or statistics • Examples: differential diagnoses, treatment options Alerts & reminders– Alerts & reminders • Based on specified logical conditions • Examples: drug-allergy checks, drug-drug interaction checks, drug-lab interaction checks, drug-formulary checks, reminders for preventive services or certain actions (e.g. smoking cessation), clinical practice guideline integration – Evidence-based knowledge sources e.g. drug database, literature – Simple UI designed to help clinical decision making
  • 33. Clinical Decision Support Systems (CDSSs) PATIENT P tiPerception Attention CLINICIAN External MemoryLong Term Memory Attention External Memory Knowledge Data Long Term Memory Knowledge Data Working Memory Knowledge DataKnowledge Data InferenceInference DECISIONFrom a teaching slide by Don Connelly 2006 DECISIONFrom a teaching slide by Don Connelly, 2006
  • 34. Clinical Decision Support Systems (CDSSs) PATIENT P tiPerception Attention CLINICIAN Abnormal lab highlights External MemoryLong Term Memory Attention highlights External Memory Knowledge Data Long Term Memory Knowledge Data Working Memory Knowledge DataKnowledge Data InferenceInference DECISIONDECISION
  • 35. Clinical Decision Support Systems (CDSSs) PATIENT P tiPerception Attention CLINICIAN Order Sets External MemoryLong Term Memory Attention External Memory Knowledge Data Long Term Memory Knowledge Data Working Memory Knowledge DataKnowledge Data InferenceInference DECISIONDECISION
  • 36. Clinical Decision Support Systems (CDSSs) PATIENT P tiPerception Attention CLINICIAN Drug-Allergy Checks External MemoryLong Term Memory Attention Checks External Memory Knowledge Data Long Term Memory Knowledge Data Working Memory Knowledge DataKnowledge Data InferenceInference DECISIONDECISION
  • 37. Clinical Decision Support Systems (CDSSs) PATIENT P tiPerception Attention CLINICIAN Drug-Drug Interaction Checks External MemoryLong Term Memory Attention External Memory Knowledge Data Long Term Memory Knowledge Data Working Memory Knowledge DataKnowledge Data InferenceInference DECISIONDECISION
  • 38. Clinical Decision Support Systems (CDSSs) PATIENT P tiPerception Attention CLINICIAN Clinical Practice Guideline External MemoryLong Term Memory Attention Reminders External Memory Knowledge Data Long Term Memory Knowledge Data Working Memory Knowledge DataKnowledge Data InferenceInference DECISIONDECISION
  • 39. Clinical Decision Support Systems (CDSSs) PATIENT P ti Integration of Evidence-Based Resources (e gPerception Attention CLINICIAN Resources (e.g. drug databases, literature) External MemoryLong Term Memory Attention External Memory Knowledge Data Long Term Memory Knowledge Data Working Memory Knowledge DataKnowledge Data InferenceInference DECISIONDECISION
  • 40. Clinical Decision Support Systems (CDSSs) PATIENT P tiPerception Attention CLINICIAN External MemoryLong Term Memory Attention External Memory Knowledge Data Long Term Memory Knowledge Data Working Memory Knowledge DataKnowledge Data Inference Diagnostic/TreatmentInference DECISION Diagnostic/Treatment Expert Systems DECISION
  • 41. Clinical Decision Support Systems (CDSSs) Issues CDSS l t l t f li i i ?• CDSS as a supplement or replacement of clinicians? – The demise of the “Greek Oracle” model (Miller & Masarie, 1990) The “Greek Oracle” Model The “Fundamental Theorem” (Friedman, 2009)
  • 42. Clinical Decision Support Systems (CDSSs) Issues F t ith i d li i l ti (K t t l 2005)• Features with improved clinical practice (Kawamoto et al., 2005) – Automatic provision of decision support as part of clinician workflow – Provision of recommendations rather than just assessmentsj – Provision of decision support at the time and location of decision making – Computer based decision support • Usability & impact on productivity
  • 43. Clinical Decision Support Systems (CDSSs) Issues Al t iti it & l t f ti• Alert sensitivity & alert fatigue
  • 44. Clinical Decision Support Systems (CDSSs) Issues Ethi l l l i• Ethical-legal issues – Liabilities: Clinicians as “learned intermediaries” – Prohibition of certain transactions vs. Professional autonomyy (see Strom et al., 2010) • Unintended Consequences (e.g. workarounds) See Koppel et al (2005) Campbell et al (2006) & Harrison et al (2007)– See Koppel et al. (2005), Campbell et al. (2006) & Harrison et al. (2007)
  • 45. Clinical Decision Support Systems (CDSSs) Issues Ch i th i ht CDSS t t i• Choosing the right CDSS strategies • Expertise required for proper CDSS design & implementation • Integration into the point of care with minimal productivity/• Integration into the point of care with minimal productivity/ workflow impacts • Everybody agreeing on the “rules” to be enforcedy y g g • Maintenance of the knowledge base • Evaluation of effectiveness
  • 46. Nursing Applications Functions D t i t i t ti & t• Documents nursing assessments, interventions & outcomes • Facilitates charting & vital sign recording • Utilizes standards in nursing informatics• Utilizes standards in nursing informatics • Populates and documents care-planning • Risk/incident managementRisk/incident management • etc. Issues • Minimizing workflow/productivity impacts • Goal: Better documentation vs. better care? • Evolving standards in nursing practice • Change management
  • 47. Pharmacy Applications Functions St li kfl f di ti d t di i d• Streamlines workflow from medication orders to dispensing and billing • Reduces medication errors improves medication safetyReduces medication errors, improves medication safety • Improves inventory management
  • 48. Stages of Medication Process Ordering Transcription Dispensing Administration CPOE Automatic Medication Dispensing Electronic Medication AdministrationDispensing Administration Records (e-MAR) Barcoded Barcoded Medication Administration Barcoded Medication Dispensing d s a o
  • 49. Pharmacy Applications Issues Wh t di ti d i t l t i f t t hi h• Who enters medication orders into electronic format at which stage? • Unintended consequencesUnintended consequences • “Power shifts” • Handling exceptions (e.g. countersigns, verbal orders,g p ( g g , , emergencies, formulary replacements, drug shortages) • Choosing the right technology for the hospital • Goal: Workflow facilitation vs. medication safety?
  • 50. Imaging Applications Picture Archiving and Communication System (PACS) C t hi d di l l t i i t d f• Captures, archives, and displays electronic images captured from imaging modalities (DICOM format) • Often refers to radiologic images but sometimes used in otherOften refers to radiologic images but sometimes used in other settings as well (e.g. cardiology, endoscopy, pathology, ophthalmology) • Values: reduces space, costs of films, loss of films, parallel viewing, remote access, image processing & manipulation, referralsreferrals Radiology Information System (RIS) or Workflow Management • Supports workflow of the radiology department, including patientSupports workflow of the radiology department, including patient registration, appointments & scheduling, consultations, imaging reports, etc.
  • 51. Take-Away Messages • Health IT in hospitals comes in various forms L l t t i t t id ti• Local contexts are important considerations • Hospital IT is a very complex environment • Health IT has much potential to improve quality & efficiency of care• Health IT has much potential to improve quality & efficiency of care • But it is also risky... – Costs – Change resistance – Poor design Alert fatigue– Alert fatigue – Workarounds and unintended consequences – Use of wrong technology to fix the wrong process for the wrong goal • We need to have an informatician’s mind (not just a technologist’s mind) to help us navigate through the complexities
  • 52. Next: Health ITNext: Health IT Beyond HospitalsBeyond Hospitals
  • 53. ReferencesReferences • Amarasingham R, Plantinga L, Diener‐West M, Gaskin DJ, Powe NR. Clinical information  h l d l l h l d h dtechnologies and inpatient outcomes: a multiple hospital study. Arch Intern Med.  2009;169(2):108‐14. • Balas EA, Austin SM, Mitchell JA, Ewigman BG, Bopp KD, Brown GD. The clinical value of  computerized information services A review of 98 randomized clinical trials Arch Fam Medcomputerized information services. A review of 98 randomized clinical trials. Arch Fam Med.  1996;5(5):271‐8. • Borzekowski R. Measuring the cost impact of hospital information systems: 1987‐1994. J Health  Econ. 2009;28(5):939‐49.; ( ) • Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences  related to computerized provider order entry. J Am Med Inform Assoc. 2006 Sep‐Oct;13(5):547‐56. • Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG. Systematic  review: impact of health information technology on quality, efficiency, and costs of medical care.  Ann Intern Med. 2006;144(10):742‐52. • DeLone WH, McLean ER. Information systems success: the quest for the dependent variable.  I f S R 1992 M 3(1) 60 95Inform Syst Res. 1992 Mar;3(1):60‐95. • Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc. 2009  Apr;16(2):169‐170. 
  • 54. ReferencesReferences • Garg AX, Adhikari NKJ, McDonald H, Rosas‐Arellano MP, Devereaux PJ, Beyene J, et al. Effects of  computerized clinical decision support systems on practitioner performance and patient outcomes: a  systematic review JAMA 2005;293(10):1223 38systematic review. JAMA. 2005;293(10):1223‐38. • Harrison MI, Koppel R, Bar‐Lev S. Unintended consequences of information technologies in health  care‐‐an interactive sociotechnical analysis. J Am Med Inform Assoc. 2007 Sep‐Oct;14(5):542‐9. • Kaushal R Shojania KG Bates DW Effects of computerized physician order entry and clinical decision• Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision  support systems on medication safety: a systematic review. Arch. Intern. Med. 2003;163(12):1409‐ 16. • Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision p g p g support systems: a systematic review of trials to identify features critical to success. BMJ. 2005 Apr  2;330(7494):765. • Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of computerized physician  d f l d ( )order entry systems in facilitating medication errors. JAMA. 2005 Mar 9;293(10):1197‐1203.  • Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical diagnostic systems.  Methods Inf Med. 1990 Jan;29(1):1‐2.  P t ST D b JL I h lth i f ti t h l i t t l t d t th fi i l• Parente ST, Dunbar JL. Is health information technology investment related to the financial  performance of US hospitals? An exploratory analysis. Int J Healthc Technol Manag. 2001;3(1):48‐58. • Shiffman RN, Liaw Y, Brandt CA, Corb GJ. Computer‐based guideline implementation systems: a  systematic review of functionality and effectiveness J Am Med Inform Assoc 1999;6(2):104‐14systematic review of functionality and effectiveness. J Am Med Inform Assoc. 1999;6(2):104 14.
  • 55. ReferencesReferences • Strom BL, Schinnar R, Aberra F, Bilker W, Hennessy S, Leonard CE, Pifer E. Unintended effects of a  computerized physician order entry nearly hard‐stop alert to prevent a drug interaction: a  randomized controlled trial Arch Intern Med 2010 Sep 27;170(17):1578 83randomized controlled trial. Arch Intern Med. 2010 Sep 27;170(17):1578‐83. • Theera‐Ampornpunt N. Adopting Health IT: What, Why, and How? Presented at: How to Implement  World Standard Hospital IT?; 2010 Nov 3; Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand. Invited speaker, in Thai. U e s ty, o ae , a a d. ted spea e , a . http://www.slideshare.net/nawanan/adopting‐health‐it‐what‐why‐and‐how • Van Rosse F, Maat B, Rademaker CMA, van Vught AJ, Egberts ACG, Bollen CW. The effect of  computerized physician order entry on medication prescription errors and clinical outcome in  pediatric and intensive care: a systematic review. Pediatrics. 2009;123(4):1184‐90.