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Introduction to Health Informatics and Health IT in Clinical Settings (Part 3) (February 19, 2020)
The document provides a comprehensive overview of health information technology (HIT) in clinical settings, emphasizing its significance in improving healthcare outcomes and operational efficiency. It outlines various components and systems of hospital IT, such as electronic health records (EHRs), clinical decision support systems (CDSS), and their benefits, including enhanced patient documentation and safety. Additionally, it highlights the challenges and risks associated with HIT implementation, including resistance to change, design flaws, and alert fatigue.
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Introduction to Health Informatics and Health IT in Clinical Settings (Part 3) (February 19, 2020)
- 1. Health IT inClinical Settings Nawanan Theera-Ampornpunt, MD, PhD February 19, 2020 SlideShare.net/Nawanan Except where citing other works
- 2. IT Decision Makingin Hospitals: Key Points • Depends on local context • IT is not alone -> Business-IT alignment/integration • “Know your organization” • View IT as a tool for something else, not the end goal by itself • Focus on the real goals (what define “success”)
- 3. Success of ITImplementation DeLone & McLean (1992)
- 4. Success of ITImplementation System Quality • System performance (response time, reliability) • Accuracy, error rate • Flexibility • Ease of use • Accessibility
- 5. Success of ITImplementation Information Quality • Accuracy • Currency, timeliness • Reliability • Completeness • Relevance • Usefulness
- 6. Success of ITImplementation Use • Subjective (e.g. asks a user “How often do you use the system?”) • Objective (e.g. number of orders done electronically) User Satisfaction • Satisfaction toward system/information • Satisfaction toward use
- 7. Success of ITImplementation Individual Impacts • Efficiency/productivity of the user • Quality of clinical operations/decision-making Organizational Impacts • Faster operations, cost & time savings • Better quality of care, better aggregate outcomes • Reputation, increased market share • Increased service volume or patient retention
- 8. NOW, WHAT ARESOME IMPORTANT HOSPITAL IT?
- 9. Examples of HospitalIT Enterprise-wide • Infrastructural IT (e.g. hardware, OS, network, web, e-mail) • Office Automation • MPI, ADT • EHRs/EMRs/HIS/CIS • CPOE & CDSSs • Nursing applications • Billing, Claims & Reimbursements • MIS, ERP, CRM, DW, BI
- 10. Examples of HospitalIT Departmental Applications • Pharmacy applications • LIS, PACS, RIS • Specialized applications (ER, OR, LR, Anesthesia, Critical Care, Dietary Services, Blood Bank) • Incident management & reporting system • E-Learning • Clinical research informatics
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- 12. Infrastructural IT • HW/SWAcquisition, installation & maintenance • System administration • Network administration • Security
- 13. Infrastructural IT Issues • Expertise •Insourcing vs. Outsourcing • Policy & Process Controls • Best Practices in Design & Management • Documentation!!! • Risks – Confidentiality/Integrity – Outages – Redundancy vs. Cost – Configuration complexities & patch management – Compatibility & Technology Choices
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- 15. 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 applications • Issues – A large database – Interface with other systems – Duplicate resolutions – Accuracy & currency of patient information – Language issues
- 16. Admission-Discharge-Transfer (ADT) • Functions –Supports Admission, Discharge & Transfer of patients (“patient management”) – Provides status/location of admitted patients – Used in assessing bed occupancy – Linked to billing, claims & reimbursements • 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) – Input of important information (diagnoses, D/C summary) – Links between OPD, IPD, ER & OR
- 17. EHRs & HIS TheChallenge - Knowing What It Means Electronic Medical Records (EMRs) Computer-Based Patient Records (CPRs) Electronic Patient Records (EPRs) Electronic Health Records (EHRs) Personal Health Records (PHRs) Hospital Information System (HIS) Clinical Information System (CIS)
- 18. EHRs Commonly Accepted Definitions •Electronic documentation of patient care by providers • Provider has direct control of information in EHRs • Synonymous with EMRs, EPRs, CPRs • Sometimes defined as a patient’s longitudinal records over several “episodes of care” & “encounters” (visits)
- 19. EHR Systems Are theyjust a system that allows electronic documentation of clinical care? Or do they have other values? Diag- nosis History & PE Treat- ments ...
- 20. Documented Benefits ofHealth 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 (Kaushal et al, 2003;Chaudhry et al, 2006;van Rosse et al, 2009) – Patient surveillance & monitoring (Chaudhry et al, 2006) – Patient education/reminder (Balas et al, 1996) – Cost savings and better financial performance (Parente & Dunbar, 2001;Chaudhry et al, 2006;Amarasingham et al, 2009; Borzekowski, 2009)
- 21. Functions that ShouldBe 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) • Discharge Summaries (Blumenthal et al, 2006) • Diagnostic Test Results (Blumenthal et al, 2006) • Radiologic Reports (Blumenthal et al, 2006)
- 22. EHR Systems/HIS: Issues •Functionality & workflow considerations • Structure & 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) – Integration with paper systems (e.g. scanned MRs, legal documents) • Reliability & contingency/business continuity planning • Roll-out strategies & change management • Interfaces
- 23. Computerized (Physician/Provider) OrderEntry Functions • Physician directly enters medication/lab/diagnostic/imaging orders online • Nurse & pharmacy process orders accordingly • Maybe considered part of an EHR/HIS system
- 24. Values • No handwriting!!! •Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency Computerized Provider Order Entry (CPOE)
- 25. Computerized (Physician/Provider) OrderEntry Issues • “Physician as a clerk” frustration • Usability -> Reduced physician productivity? • Unclear value proposition for physician? • Complexity of medication data structure • Integration 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 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”
- 26. Nursing Applications Functions • Documentsnursing assessments, interventions & outcomes • Facilitates charting & vital sign recording • Utilizes standards in nursing informatics • Populates and documents care-planning • Risk/incident management • etc. Issues • Minimizing workflow/productivity impacts • Goal: Better documentation vs. better care? • Evolving standards in nursing practice • Change management
- 27. Pharmacy Applications Functions • Streamlinesworkflow from medication orders to dispensing and billing • Reduces medication errors, improves medication safety • Improves inventory management
- 28. Stages of MedicationProcess & Closed Loop Medication Management Ordering Transcription Dispensing Administration CPOE Automatic Medication Dispensing Electronic Medication Administration Records (e-MAR) Barcoded Medication Administration Barcoded Medication Dispensing
- 29. Pharmacy Applications Issues • Whoenters medication orders into electronic format at which stage? • Unintended consequences • “Power shifts” • Handling exceptions (e.g. countersigns, verbal orders, emergencies, formulary replacements, drug shortages) • Choosing the right technology for the hospital • Goal: Workflow facilitation vs. medication safety?
- 30. Imaging Applications Picture Archivingand Communication System (PACS) • Captures, archives, and displays electronic images captured from imaging modalities (DICOM format) • Often 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, referrals Radiology Information System (RIS) or Workflow Management • Supports workflow of the radiology department, including patient registration, appointments & scheduling, consultations, imaging reports, etc.
- 31. • The realplace where most of the values of health IT can be achieved – Expert systems • Based on artificial intelligence, machine learning, rules, or statistics • Examples: differential diagnoses, treatment options(Shortliffe, 1976) Clinical Decision Support Systems (CDS)
- 32. – Alerts &reminders • Based on specified logical conditions • Examples: –Drug-allergy checks –Drug-drug interaction checks –Reminders for preventive services –Clinical practice guideline integration Clinical Decision Support Systems (CDS)
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- 34. • Reference informationor evidence- based knowledge sources –Drug reference databases –Textbooks & journals –Online literature (e.g. PubMed) –Tools that help users easily access references (e.g. Infobuttons) More CDS Examples
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- 36. • Pre-defined documents –Ordersets, personalized “favorites” –Templates for clinical notes –Checklists –Forms • Can be either computer-based or paper-based Other CDS Examples
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- 38. • Simple UIdesigned to help clinical decision making –Abnormal lab highlights –Graphs/visualizations for lab results –Filters & sorting functions Other CDS Examples
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- 40. External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
- 41. External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Abnormal lab highlights
- 42. External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Drug-Allergy Checks
- 43. External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Drug-Drug Interaction Checks
- 44. External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Clinical Practice Guideline Reminders
- 45. External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Diagnostic/Treatment Expert Systems
- 46. What words cometo mind when you hear... Digital Health Transformation
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- 49. Image Source: englishmoviez.com Riseof the Machines?
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- 51. Image Source: amazon.com SmartPhones, Dumb People?
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- 53. “Big data islike teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...” -- Dan Ariely @danariely (2013) Substitute “Big data” with “AI”, “Blockchain”, “IoT” of your choice. -- Nawanan Theera-Ampornpunt (2018) “Teenage Sex” of IT
- 54. Hype vs. Hope JeremyKemp via http://en.wikipedia.org/wiki/Hype_cycle http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
- 55. Gartner Hype Cycle2017 https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/
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- 57. A Real-Life PersonalStory of My Failure (as a Doctor and as a Son) in Misdiagnosing My Mom Would AI Help?
- 58. • Nothing iscertain in medicine & health care • Large variations exist in patient presentations, clinical course, underlying genetic codes, patient & provider behaviors, biological responses & social contexts Why Clinical Judgment Is Still Necessary?
- 59. • Most diseasesare not diagnosed by diagnostic criteria, but by patterns of clinical presentation and perceived likelihood of different diseases given available information (differential diagnoses) • Human is good at pattern recognition, while machine is good at logic & computations Why Clinical Judgment Is Still Necessary?
- 60. • Machines are(at best) as good as the input data –Not everything can be digitized or digitally acquired –Not everything digitized is accurate (“Garbage In, Garbage Out”) • Experience, context & human touch matters Why Clinical Judgment Is Still Necessary?
- 61. • CDSS asa replacement or supplement of clinicians? – The demise of the “Greek Oracle” model (Miller & Masarie, 1990) The “Greek Oracle” Model The “Fundamental Theorem” Model Friedman (2009) Wrong Assumption Correct Assumption Proper Roles of CDS
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- 63. Being Smart #1: StopYour “Drooling Reflex”!!
- 64. Being Smart #2: Focuson Information & Process Improvement, Not Technology
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- 66. Why Aren’t WeTalk About These Words? http://hcca-act.blogspot.com/2011/07/reflections-on-patient-centred-care.html
- 67. The Goal ofHealth Care The answer is already obvious... “Health” “Care”
- 68. • Safe • Timely •Effective • Patient-Centered • Efficient • Equitable Institute of Medicine, Committee on Quality of Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2001. 337 p. High Quality Care
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- 70. Some risks • Alertfatigue Unintended Consequences of Health IT
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- 72. Clinical Decision SupportSystems (CDSSs) Issues • Choosing the right CDSS strategies • Expertise required for proper CDSS design & implementation • Integration into the point of care with minimal productivity/ workflow impacts • Everybody agreeing on the “rules” to be enforced • Maintenance of the knowledge base • Evaluation of effectiveness
- 73. “Ten Commandmends” forEffective CDSSs • Speed is Everything • Anticipate Needs and Deliver in Real Time • Fit into the User’s Workflow • Little Things (like Usability) Can Make a Big Difference • Recognize that Physicians Will Strongly Resist Stopping • Changing Direction Is Easier than Stopping • Simple Interventions Work Best • Ask for Additional Information Only When You Really Need It • Monitor Impact, Get Feedback, and Respond • Manage and Maintain Your Knowledge-based Systems (Bates et al., 2003)
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- 75. Hospital A HospitalB Clinic C Government Lab Patient at Home Health Information Exchange
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- 78. Take-Away Messages • HealthIT in clinical settings comes in various forms • Local contexts are important considerations • Clinical IT is a very complex environment • Health IT has much potential to improve quality & efficiency of care • But it is also risky... – Costs – Change resistance – Poor design – 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
- 79. References • Amarasingham R,Plantinga L, Diener-West M, Gaskin DJ, Powe NR. Clinical information technologies 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 Med. 1996;5(5):271-8. • Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, Spurr C, Khorasani R, Tanasijevic M, Middleton B. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003 Nov-Dec;10(6):523-30. • 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. Inform Syst Res. 1992 Mar;3(1):60-95.
- 80. References • Friedman CP.A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc. 2009 Apr;16(2):169-170. • 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):1223-38. • 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 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 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 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. • 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.
- 81. References • 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):104-14. • 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):1578-83. • 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. 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.