INFO-I642 Clinical Decision Support Systems

1. INFO-I642 (Clinical Decision Support Systems) CDSS in Clinical Practice (Intermountain Healthcare) Lecture #9

2. Lecture in a Nutshell HELP System Introduction The Help System Key Features for CDSS CDSS Categories Alerting Systems Critiquing Systems Suggestion Systems CDSS for Information Management Pharmacy System Blood Gas Reports Emergency Department Infection Report Nurse Bedside Charting Respiratory Therapy Charting CDSS for Focusing Attention Infectious Disease Monitor Summary Therapeutic Antibiotic Monitor Adverse Drug Event Monitor Lab Alerts

3. Intermountain Healthcare (IHC) CDSS

4. Health Evaluation through Logical Processing(HELP System)

5. Introduction Growing dependency on computers to maintain part or all of the medical record ? increased dependency on automated medical decision making to support the delivery of economical, quality care. 1999: IOM estimated that between 44,000 and 98,000 Americans die each year because of medical errors. 25 years of CDSS delivered through the HELP (Health Evaluation through Logical Processing) System at IHC. Intention for longitudinal medical record ? CDSS in inpatient and outpatient setting. Inpatient application: HELP Hospital Information System (HIS) located at the LDS Hospital in Salt Lake City. Teams from IHC, the Department of Medical Informatics of the University of Utah, and commercial partners developed these applications. An enterprise CDSS implies an enterprise model for knowledge management ? easier integration of several new types of decision support into clinical computing environments.

6. The Help System Hardware: operates on HP NonStop Enterprise Division. Software: various components installed in > 20 hospitals operated by IHC. IHC Central: LDS Hospital, Utah The HELP System consists of an integrated clinical database, a framebased medical DSS, programs to support hospital and departmental clinical and administrative functions. Data is entered either manually at the hospital terminal or collected automatically from patients (ICU). The HELP System contains a decision support subsystem based on a modular representation of medical decision logic in frames ? decisions encoded in these are written in a Pascal-like language ? they are each designed to represent a single simple decision capable of activation in a number of ways. HELP ? successful development of expert systems in blood gas interpretation, intensive care settings, and medication monitoring. HELP implemented two types of CDSS systems: (1) narrowly circumscribed medical conditions ? the logic is typically simple and the data requirements modest (2) diagnostic entities using raw medical data ? challenging task of managing large degrees of uncertainty using pattern matching algorithms.

7. The Help System cont.

8. Key Features for CDSS Tools in HELP Unlike most computer systems used in hospitals during the 1970s, HELP was designed from the outset as a clinical system, specifically with the aims of being able to provide decision support and to be used as a research tool. HELP contained the data elements that are billable, such as laboratory tests, and the codes that are sent to the financial system. Key features of HELP: Integration with EHR ? data are stored in a coded format. Coded data are needed for the decision support process. HELP System is a knowledge base that contains thousands of medical logic modules (MLMs): first part identifies which data elements in the EHR are needed for the logic, and the second part contains the computer logic used to analyze the data elements. HELP System is the ability to data- and time-drive the knowledge base. The data in the EHR are never deleted. All clinical patient data from the HELP System since 1983 are stored in the current or archival EHRs ? are often analyzed and used to develop the medical logic contained in the MLMs.

9. Key Features for CDSS Tools in HELP cont.

10. Key Features for CDSS Tools in HELP cont.

11. CDSS Categories Elements of CDSS that are critical to success: the mechanism by which the systems acquire the data used in their decision algorithms the interface through which they interact with clinicians to report their results CDSS Categories: Respond to clinical data by issuing an alert Activated in response to recorded decisions ? critiquing the decision and proposing alternative suggestions as appropriate Respond to a request by the decision maker by suggesting a set of diagnostic or therapeutic maneuvers fitted to the patient�s needs Retrospective quality assurance applications where clinical data are abstracted from patient records and summary decisions about the quality of care are made and fed back to caregivers

12. Alerting Systems Alerting processes are programs that function continuously, monitoring select clinical data as it is stored in the patient�s electronic record. They are designed to test specific types of data against predefined criteria. If the data meet the criteria, these systems alert medical personnel. HELP lab checking module ? detects and alerts for potentially life-threatening abnormalities in the data acquired. They have simple decision logic. The �times� when the data are reviewed have only a loose relationship to the �times� when these data become available ? physicians only reviewing the results twice a day ? abnormalities in laboratory results may not receive the timely attention they deserve. Computerized Laboratory Alerting System (CLAS): brings potentially life-threatening conditions (10 most important) to the attention of caregivers. Rules were developed by medical experts. Delivering methods: (1) Active: a flashing light in a nursing division alerting abnormal lab values (2) Passive: alert while reviewing patient records CDSS Categories cont.

13. CDSS Categories cont.

14. Studies showed an increase in appropriate therapy for conditions involving abnormalities of Na+, K+, and glucose. IHC Outpatient: This type of decision support intervention is becoming increasingly common as hospital information systems evolve. IHC Inpatient: asked personnel telephone ordering physicians or other caregivers whenever critical laboratory values are detected. Example: They used Chronic Anticoagulation Clinic�s (CAC) anticoagulation protocol to adjust INR (International Normalized Ratio) for the patients. Alerts for dangerously altered INRs are also sent to the clinic nurse practitioner�s pager so that immediate action can be taken. Critiquing Systems Critiquing processes begin functioning when an order for a medical intervention is entered into the information system. Such methods typically respond by evaluating an order and either pointing out disparities between the order and an internal definition of proper care or by proposing an alternative therapeutic approach. CDSS Categories cont.

15. In 1987, the Joint Commission for the Accreditation of Healthcare Organizations (JCAHO) began to require healthcare institutions to develop criteria for the use of blood products ? LDS computerized the process. Embedded in the blood-ordering program is a critiquing tool designed to ascertain the reason for transfusion and compare the reason against criteria. A list of reasons, specific to the blood product chosen, is displayed, and the user chooses the appropriate rationale for the intervention, then CDSS checks if the order meets the hospital guidelines. Physicians can override the system. In 12.9% of the system�s total uses, there were no orders at end. CDSS Categories cont.

16. Suggestion Systems This highly interactive group of processes is designed to react to requests (either direct or implied) for assistance. These processes respond by making concrete suggestions concerning which actions should be taken next. The suggestions are expected (not like alerting systems) and physicians need not commit to an order (not like critiquing systems) before the program applies its stored medical logic. 1987: Ventilator therapy at ICU LDS for patients with Adult Respiratory Distress Syndrome (ARDS). The usual therapy includes respiratory support while the underlying pulmonary injury heals; however, research began to suggest that external devices that bypassed the lungs to remove carbon dioxide directly might improve survival in ARDS ? researchers decided to standardize care by strict adherence to predetermined treatment protocols in an RCT ? they developed them eventually as CDSS ? didn�t find difference between control and treatment but control had higher survival rate than usual ? quality and uniformity of care provided through the use of computerized protocols had resulted in an important improvement in patient outcomes. CDSS Categories cont.

17. CDSS Categories cont.

18. CDSS for Information Management Computer applications that fall under this category are programs that manage the entry, storage, retrieval, and reporting of patient information. Overall, these systems definitely improve the quality and safety of patient care by making information more accessible and easier to read. Pharmacy System The pharmacy application developed on the HELP System accesses patient data from the EHR to generate alerts of potential adverse drug events; drug�drug, drug�allergy, drug�laboratory, drug�disease, drug�dose, drug�diet, and drug�interval. The alerts are displayed to the pharmacists as they enter the hand-written physician orders into the application ? 5%of patients and 0.8% of drug orders generated alerts, and that physicians changed patient therapy for 77% of the alerts. The problem with this approach was that patients may receive the drugs before the pharmacists enter the orders into the pharmacy application ? using CPOE eliminated the problem.

19. Blood Gas Reports The early development of the HELP System paralleled a number of advances in arterial blood gas (ABG) analysis, and the initial use of computers to provide interpretation of blood gas results. The blood acid-base map was modified based on the altitude of Salt Lake City, Utah, and used to develop decision support logic. 80% of the ABG interpretations were helpful and 28% changed patient care. The standard interpretation of blood gas data facilitated the development of the computer logic to interpret the blood gas results. Emergency Department Infection Report In ERs often the laboratory tests are ordered only for precautionary purposes and the patients are sent home. The report printout all the microbiology and other infection-related test results for all the emergency department patients during the past 10 days. When important information is found, the patients are contacted and given specific instructions based on the test results. CDSS for Information Management cont.

20. Nurse Bedside Charting In the 1980s, an electronic nurse-charting program was developed on the HELP System ? contained some decision logic that would alert the nurse when patient information that was entered was out of range or inappropriate. Increase the charting of nursing information from 59% to 73% and charting at the bedside also increased from 40% to 63%. Respiratory Therapy Charting Graduate thesis from medical informatics program ? provided automatic billing, management functionality, alerts, and reports to RTs. The respiratory therapists (RT) found the new system so functional that it completely replaced the old manual methods. A comparison of the portable computers to the ward computer terminals showed no difference in productivity. Due to the need to carry the portable computers around and the extra time to connect to phone jacks (no wireless connection at that time), the therapists preferred using the ward computer terminals. CDSS for Information Management cont.

21. CDSS for Focusing Attention Infectious Disease Monitor 1985: Notifies (1) patients with hospital acquired infections (2) patients with reportable diseases (3) patients with antibiotic-resistant pathogens (4) patients with infections in sterile body sites (e.g., blood, CSF, PF). MLMs contain logic to determine which infections need to be reported to state and federal health departments whereas others contain the CDC criteria for the identification of hospital-acquired infections. The program builds a listing of reports that is sent to the printer in the infectious disease or quality assurance offices. Since microbiology cultures and antibiotic susceptibilities take days or weeks to complete, many of the alerts are first generated based on Gram stain and other preliminary findings and results. The text �VERIFIED� included at the bottom of the report notifies the infection control practitioner that the culture is now finalized by the microbiology and computer logic has made its final decision.

22. CDSS for Focusing Attention cont.

23. CDSS for Focusing Attention cont.

24. Therapeutic Antibiotic Monitor Goal: identify patients who may not be receiving appropriate antibiotics based on the results from microbiology culture and susceptibility results. The application process is similar to the infectious disease monitor. The patient�s current antibiotics are retrieved from the EHR and compared with the list of appropriate antibiotics. If the patient is not receiving an appropriate antibiotic, an alert is sent to the pharmacist and then physician. The monitor selected the less expensive antibiotic from a list of clinically equal antibiotics. 1990: In one year 420 alerts, 30% change in therapy. When contacted by the pharmacists, the physicians stated that they were not aware of the culture and susceptibility results 49% of the time. Adverse Drug Event Monitor 2% to 10% of inpatients experience ADE by average. Goal: identify patients who may not be receiving appropriate antibiotics based on the results from microbiology culture and susceptibility results. CDSS for Focusing Attention cont.

25. MLMs were developed that monitored: (1) laboratory test results that could be indicative of a possible ADE (2) elevated serum drug levels (3) the ordering of drugs that are commonly used to treat ADEs (4) physiologic data that could signal possible ADEs. Early physician notification of mild and moderate ADEs by pharmacists helped to decrease the number of severe ADEs. It was further developed into adverse medical device event (AMDE) monitor. Lab Alerts Medical personnel are unaware of the printed lab abnormalities until the results are reviewed. The computerized laboratory alerting system (CLAS) displayed an alert message to the user the next time the laboratory test results were reviewed for the patients. An enhanced version was tried in which yellow lights were attached to each computer terminal and would flash as soon as an alert was generated for a patient on that nursing division. CDSS for Focusing Attention cont.

26. Antibiotic Duration Monitor Prolonged use of prophylactic antibiotics for surgical operations to be a major cause for the increase in antibiotic-resistant pathogens. A computer application is activated each morning by the time-driver and checks the EHR of each hospitalized patient to determine if the patient had a surgical operation and is receiving antibiotics that were initiated within 24 hours of the latest operation. Patients that are receiving prophylactic antibiotics longer than 48 hours after the operation and do not have any evidence in the EHR of infection are added to a printed list sent to the pharmacy each day. In 6 months it reduced 19 doses to 13 by average (saving of $44K) Preoperative Antibiotic Monitor Prophylactic antibiotics should be given within the two-hour time window to allow maximum antibiotic concentrations in tissue and blood during the procedure ? a computer application generated reminders of the importance of starting prophylactic antibiotics within two hours before the start of the operation and placed in selected patients� charts ? increase wound healing. CDSS for Focusing Attention cont.

27. High-Risk Alerts for Hospital-Acquired Infections Used statistical methods to identify patients at high risk of developing an infection in the hospital before the infection onset. A study database was created with patients with hospital-acquired infections and control patients. Stepwise logistic regression was used to develop a predictive model for high-risk patients based on 10 of 18 risk factors tested. A computer program was activated each day to use an equation based on the model to monitor all hospitalized patients and create a computer printout of the high-risk patients. The evaluation of the randomized process showed that there was not a significant difference in hospital-acquired infection rates between study and control patients ? identifying a patient as being at high risk of an infection was too general Decision support needs to be very specific (e.g., preoperative antibiotic monitor). CDSS for Focusing Attention cont.

28. Drug-Dose Monitor This program calculates the renal function (Cr clearance) of every hospitalized patient each day and determines whether the patient is receiving a drug dosage that is too high Excessive dosage was reduced from 4.7 to 2.9 Average antibiotic costs decreased from $128 to $98 Significantly reduced ADEs caused by certain antibiotics Enhanced Notification of Ventilator-related Events An enhanced alerting system to notify medical personnel whenever patients become disconnected for longer than 10 seconds It flashed an alert on the screen identifying the patient room + played a submarine dive horn sound. Initial evaluations show that patients are now disconnected only for an average of 20 seconds. CDSS for Focusing Attention cont.

29. CDSS for Patient-Specific Consultation Blood Ordering Medical staff electronically order blood products and are required to enter the reason before the order could be completed. The program validates the reason based on patient laboratory, surgery, and other clinical information contained in the EHR. Physicians could override the system but they were flagged and followed up by quality management - monthly reports were sent to department directors. Over time, given the fact that almost all other orders had to be handwritten, physician use of the blood products ordering program decreased to the point where most of the blood orders are entered in the computer by nurses and they use their judgment as to why the blood product was needed. Ventilator Protocols Respiratory therapists and physicians could run the computer protocols from the bedside computers and receive the ventilator recommendations for acute respiratory distress syndrome (ARDS) patients ? accuracy 92.8%, adherence to protocol 92.3%, survival rate of 67% versus 33% in control group

30. Anti-infective Agent Assistance (AAA) Since 1989 HELP collected all positive microbiology cultures with antibiotic susceptibilities for the last five-year period. Stepwise logistic regression models were used to identify patient variables contained in the infection database that can help predict which pathogens a patient may have before microbiology culture results are available. The programs then selects an appropriate antibiotic regimen for the patient based on probability of clinical success, patient allergies, toxicity, and cost. The computer were appropriate 94% of the time whereas physician-ordered antibiotics were appropriate 77% of the time. AAA ? a single screen was designed to display all patient information that physicians should be aware of for the selection process: pertinent patient information and calculations suggested anti-infective agents along with the dosage, route, and interval options to quickly access detailed patient information such as the antibiograms and empiric antibiotic predictions CDSS for Patient-Specific Consultation cont.

31. CDSS for Patient-Specific Consultation cont.

32. Therapeutic antibiotic monitor suggested antibiotics based on a single culture result while patient may have many pathogens from multiple sites ? CDSS should assimilate all anti-infective agent requirements into one regimen. AAA ? reduced the number of times: patients received inappropriate antiinfective agents, excessive anti-infective dosages, adverse drug events caused by anti-infective agents, anti-infective agents to which they had documented sensitivities, and cost of anti-infective agents. AAA was modified for the pediatric population ? new logic could determine whether the patient was a neonate, pediatric, or adult patient. Pediatric AAA ? decrease in: physician requests for pharmacy help in dosage selection, pharmacy interventions due to erroneous dosage selection, days patients received excessive dosages, days patients received sub-therapeutic dosages, number of anti-infective orders, the cost of anti-infectives. No impact on the rate of ADEs was found. 2003: The infectious disease physician determined that the computer suggestions were not blindly followed. 2004: Based on the monthly committee meetings, the computer logic has been changed every month. CDSS for Patient-Specific Consultation cont.

33. Diagnostic DSS in the Help System Diagnostic decision support systems (DDSS) differ from the CDSS. CDSS can draw attention to specific data elements and/or derive therapeutic suggestions from these elements. But in DDSS the diagnostic process is a preliminary step to suggesting therapeutic interventions. Proven Diagnostic Applications These applications use a set of raw clinical data and attempt to standardize various diagnostic categorizations that impact discrete therapeutic decisions: Adverse Drug Events WHO: �any response to a drug which is noxious, unintended, and which occurs at doses normally used in man for the prophylaxis, diagnosis, or therapy of disease.� This is difference from drug-drug interaction detection. The goal of ADE detection is to determine the existence of a drug reaction from the patient data collected during the routine recording of patient care. 1991: ADE recognition subsystem has been implemented in the HELP ? checking for signs of rash, changes in respiratory rate, heart rate, hearing, mental status, seizure, anaphylaxis, diarrhea, and fever.

34. The ADE subsystem of HELP implements a scoring system (Naranjo method) is used to score the ADEs as definite (score = 9), probable (score 5�8), possible (score 1�4), or unlikely (score 0). The ADE subsystem of HELP increased ADE reporting from 9/yr to 401/yr. Number of severe ADE events decreased from 41/yr to 12/yr events. Prevented increase in length of hospital stay by 1.91 days (~$2262). Nasocomial Infection The computerized surveillance system used in LDS Hospital relies on data from a variety of sources to diagnose nosocomial infections: microbiology laboratory, nurse charting, the chemistry laboratory, the admitting office, surgery, pharmacy, radiology, and respiratory therapy. In a study the system found 182 patients with nasocomial infection in two months compared to 145 by traditional methods. Sensitivity of the system was 90% while other methods were 76%. It used Boolean logic. An additional module used logistic regression to predict and estimate the risk of hospital-acquired infection for inpatients ? it was 63% correct. Diagnostic DSS in the Help System cont.

35. Antibiotic Assistant A tool at LDS Hospital to help clinicians make informed decisions concerning the administration of antibiotics: (1) assembles relevant data such as temperature, renal function, and allergies (2) suggests a course of therapy appropriate to that patient�s condition (3) allows the clinician to review hospital experience with infections, find relevant articles, and see the logic. Diagnostic part of is stores and uses six clinical variables in predicting the cause of infection: site of infection, the patient�s status (inpatient or outpatient), the mode of transmission (community- or hospital-acquired), the patient�s hospital service, the patient�s age, and the patient�s sex. This probabilistic knowledge is then filtered through a set of rules created by infectious disease experts. These rules adjust the output of the first phase to include criteria representing basic tenets of antibacterial therapy. The resulting knowledge base is used by the antibiotic assistant program to make presumptive diagnoses of infectious organisms and to suggest treatments appropriate to these organisms. It remains up-to-date through monthly updates of its knowledge base. Diagnostic DSS in the Help System cont.

36. Complex Diagnostic Applications An important portion of the value of computerized diagnostic tools lies in the development of well-designed models of the diagnostic process to assist in the complex clinical decision-making tasks. HELP system has a frame-based decision support subsystem capable of capturing and employing Bayes� equation to assess probabilistically the support for diagnoses provided by various combinations of clinical data. Assisting Data Collection The goal has been to identify tools that could effectively collect a medical history appropriate for use in diagnostic decision support applications Three techniques for collecting the history were explored: (1) a simple branching questionnaire (2) using frame-based Bayesian expert system analyzes all available data and determines which additional information is needed to determine a disease (3) two step paper version of #2. On average mode 2 took a significantly shorter time to run (8.2 minutes) and asked significantly fewer questions (48.8 questions) than did mode 1 (19.2 minutes and 137 questions, respectively). Diagnostic DSS in the Help System cont.

37. Diagnostic DSS in the Help System cont.

38. Assessing the Quality of Medical Reports Goal ? to develop a technique for measuring the quality of X-ray reporting without requiring the review of radiographs by multiple radiologists. Traditionally, the results of the repeated readings by multiple radiologists are used to define a �gold standard� for the films. Then the individual radiologists are compared to the gold standard. The computerized version compared the description in the report to the patient�s overall diagnostic outcome. The assumption inherent in this usage is that the information contained in an X-ray report can be expected to alter the likelihood of the various diseases that a patient might have. Shannon Information Content is calculated from the change in probability of these diseases ? X-ray readers were compared based on the bits of information produced. The �trained� radiologists produced 11% more information than the �untrained� radiologists. Diagnostic DSS in the Help System cont.

39. Health Evaluation through Logical Processing 2(HELP2 System)

40. IHC HELP2 The HELP system was built on old technology while HELP2 is web-based. In the old HELP system each EHR was on a separate database. In addition to HELP functionalities, HELP2 has additional capabilities that are due to the fact that on HELP2, all IHC hospitals, clinics, and offices share a common EHR. HELP2 has implemented unique patient number enterprise-wide. Because HELP2 is web-based it can be used remotely (remote CDSS) The new anti-infective management program being developed on HELP2 is more accurate, with access to microbiology, chest x-ray and other patient information obtained at one IHC facility before the patient is transferred to another IHC hospital. Although the future database design, architecture, and access tools are being reevaluated at IHC, the functionality of the HELP System and benefits of the integrated HELP2 System provide a stable roadmap that should be followed by any new system.

41. Infrastructure for an Enterprise CDSS in HELP2 The HELP system served as a test bed for CDSS for more than two decades. HELP2 CDSS infrastucture has five main modules: data-drive, time-drive, rule node, dispatch node, and configuration manager.

42. Data-drive is the module responsible for activating the rules whenever any clinical data are stored in the database. Data that arrive in the time-drive module can be held there for a predetermined amount of time before they are delivered to the rule node. The rule node was designed to allow wide choice in the methods used for processing the data. It can run different inference engines. The rule node receives the data and verifies which rules or protocols should be executed.Migration HELP to HELP2: Translating the data to a common data model allows the development of rules independent of the data location or structure. Rule developers have no need to know were the data are physically located and/or its structure or codes. This facilitates maintenance of the rules when migrating data from a legacy system to a new platform. The dispatch node is responsible for saving the conclusions to the EHR and delivering them to a destination specified by the user or the rule developer. A configuration manager controls the functioning of the all the modules. Infrastructure for an Enterprise CDSS in HELP2 cont.

43. IHC Clinical KM Infrastructure Transition from its legacy inpatient information system (HELP) to the new component-based clinical information system (HELP2) ? new definition has grown to embrace systems that access collections of more general advice while still respecting the context provided by a selected patient�s data. Infrastructure Overview IHC has developed problem-specific guidelines detailing features of clinical care ? delivered as textual advice or as lists of suggested orders in CPOE. Development teams and workgroups are recruited from practicing clinicians Goal: disseminate clinical best practices to help reduce clinical variability and improve disease management processes and outcomes. Tools to Manage Clinical Knowledge The software infrastructure aims at supporting distributed and collaborative processes for authoring, reviewing, and deployment of knowledge content. Knowledge content is stored and organized by a knowledge repository (KR).

44. Each KR record is considered a knowledge document that is preferably represented in XML, but many of the most common multipurpose internet mail extensions (MIME) formats are also supported. Every knowledge document is associated with a header XML document that is used to store detailed document metadata. The header is used to implement the KR�s version control mechanism, providing a detailed record of all the changes and enhancements made to any given knowledge document. Knowledge Authoring Tool (KAT): is an authoring environment that allows clinical experts to create knowledge documents using XML as the underlying representation formalism Knowledge Review Online (KRO): main function is to support an open and distributed review process, where practicing clinicians, i.e., end-users of the knowledge documents, have the opportunity to provide direct feedback to the document authors. Clinical KM Infrastructure and CPOE Both the critiquing and suggestion-based approaches are most effective in an environment where the physician has direct interaction with computer (CPOE) IHC Clinical KM Infrastructure cont.

45. The HELP2 CPOE implementation strategy is based on context specific order sets as a key factor to encourage physicians� acceptance of the new system. The effective development of order sets requires a constant collaboration between clinical experts responsible for authoring the order sets and the clinicians who use these sets. Using KAT, the author can create an order set by simply filling the template that has been designed specifically for order sets. Within KRO, every comment and suggestion regarding an order set is instantaneously made available to the author and to the other reviewers. The authoring and review cycle can be repeated several times, until the content of the order set is considered adequate for clinical use ? order set will become active and available in CPOE The authoring team is responsible for making sure the order sets are current with published evidence and accreditation requirements. Main complaints of HELP2 CPOE order sets: absence of a connected order communications system ? the physicians create their orders using CPOE but then are required to provide a printed version for further processing. IHC Clinical KM Infrastructure cont.

46. Conclusion The timing of data entry is critical. Patient information needs to be entered into the EHR as soon as possible, including interfaces, medical devices, and manual data entry. Successful decision support applications are developed by a team consisting of clinical domain experts providing the why and what needs to be done and the medical informatition providing the how. Decision support should be integrated with the daily work processes of the medical staff and occur at the appropriate point of patient care. Patient alerts should be sent directly to the most appropriate people as soon as possible. Decision support applications need to be tested for safety before they are made available for general use. One bad experience can create barriers or restrictions for any future applications. Often large patient care improvement projects need to be broken down into smaller more manageable processes. The medical logic and rules need to be evidence based and match local processes of patient care.

47. The logic and rules need to be periodically reviewed and updated as patient care and technology change. The applications must be easy to use and training should not be so difficult that patient safety could be compromised. Evaluation of medical decision support applications is often the hardest part. The applications need to be cost effective and reasonable to implement and maintain in order to gain administration support as well as clinical support. Physician support of order entry is easier to get if all orders, laboratory, medication, radiology, and so on, can be made at the same time using the same application. Conclusion cont.

48. Summary HELP System Introduction The Help System Key Features for CDSS CDSS Categories Alerting Systems Critiquing Systems Suggestion Systems CDSS for Information Management Pharmacy System Blood Gas Reports Emergency Department Infection Report Nurse Bedside Charting Respiratory Therapy Charting CDSS for Focusing Attention Infectious Disease Monitor Summary Therapeutic Antibiotic Monitor Adverse Drug Event Monitor Lab Alerts