1 Crit Care Clin 21 (2005) The electronic medical record, safety, and critical care William F. Bria II, MD, FACP a, *, M. Michael Shabot, MD, FACS, FCCM, FACMI b a Department of Internal Medicine, University of Michigan Medical School, 1500 East Medical Center Drive, 3916 Taubman Center, Ann Arbor, MI , USA b Surgical Intensive Care, Cedars-Sinai Medical Center, 9700 Beverly Boulevard, Suite 8215, Los Angeles, CA 90048, USA Since the call in 2000 by the Leapfrog Group to decrease medical errors by implementing computerized physician order entry (CPOE) as well as their recommendation of sweeping changes in intensive care unit (ICU) staffing, there has been a crescendo of publications and interest in the study of this and all components of the electronic medical record (EMR) . This call for change in implementation of information systems in American hospitals followed the 1999 Institute of Medicine (IOM) publication of To Err is Human which reported that 95,000 people in the United States die each year because of medical errors in hospitals that are preventable with improved systems, in particular, information systems . The IOM subsequently released several other publications which suggested that information technology can lead to improvements in safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity in the delivery of health care . In 2003, the IOM refined the connection between information systems and safety in patient care by asserting that a national health information infrastructure is needed to provide immediate access to complete patient information and decision-support tools for clinicians and their patients, and to capture patient safety information as a byproduct of care and use this information to design even safer delivery systems . Finally, the entire matter of the EMR in American health care achieved the highest level of endorsement when President George W. Bush declared in his 2004 State of the Union Address * Corresponding author. address: (W.F. Bria II) /05/$ see front matter D 2004 Elsevier Inc. All rights reserved. doi: /j.ccc criticalcare.theclinics.com
2 56 bria ii & shabot that by computerizing health records, we can avoid dangerous medical mistakes, reduce costs, and improve care . For the purposes of this article, the critical care EMR is separated into integrated results reporting information system (RRIS), CPOE, and clinical decision support systems (CDSS). This article describes the elements of the EMR with regard to critical care and the special challenges and opportunities for improvement in patient safety, quality of care, and efficiency of care that are possible with modern day EMRs. Integrated results reporting information system CareWeb: case study The most commonly available components of the EMR in American ICUs is the RRIS. Systems that often provide patient-centric views of laboratory, radiology, cardiology, pharmacy, and other clinical departments data have transformed the process of information retrieval, reporting, and integration. The origins of modern day critical care RRISs usually were represented by the STAT laboratory printer which spewed out the latest blood work results; these strips of data were cut and pasted onto endless flow charts and paper records. Hand-transcribed patient flow charts consumed untold hours of house officer and nursing time in an effort to make sense of the volumes of information that were generated about the critically ill patient. More recently, RRIS systems were developed that use web-based technology, the value and challenges of which may be best understood by an examination of a case history of an RRIS. In 1997, the University of Michigan Hospitals and Health Systems (UMHHS) evaluated its clinical information environment for any impending Y2K review and to formulate a new strategy for the coming millennium. Analysis revealed that the clinical information catalog consisted of more than 400 applications that were supported in more than 60 departments in the medical center. It was clear that this situation resulted in increased support costs and maintenance for the organization. Even more concerning was the fact that these systems duplicated basic functions, such as information retrieval and presentation. This analysis made it clear that a patient-centric results reporting information system, built upon a central data repository (CDR), was needed. At that time, fortuitously, programming expertise in web-based architecture was growing the medical center information technology under the direction of Dr. Jocelyn DeWitt, CIO at UMHHS. The web technology was attractive because it was evident that the architecture would provide rapid accessibility and require little training. It also was hoped that it would decrease the cost of maintenance by standardizing systems from the standpoint of information access and integration thereby avoiding the mass duplication of information access that existed. The system was dubbed CareWeb. The system was developed in incremental steps that were directed at specific information problems/issues within the organization. For example, the first problem was to combine information from a legacy homegrown patient scheduling
3 electronic medical record safety and critical care 57 system with the data from a new commercial scheduling system. The vendor of the legacy scheduling system had no integration product; Care Web was given the task and it performed well. In particular, the incorporation of patient schedule information by clinician into easily selectable lists (Fig. 1) allowed for rapid information access in a intuitive manner for physicians, nurses, and others. From the outset, concern about inappropriate information access led to two security constructs role-based information access and robust audit trail records. Role-based information access enabled selected portions of the CareWeb system for some users and restricted access for others. For example, the Psychiatry Department judged that its clinical documentation was too sensitive to be widely available for viewing. Therefore, based upon user access (double-bind password system), psychiatry notes would be viewable to the user or not. The importance of complete information for patient safety (eg, medication interactions) led to the requirement that the problem summary list always remained complete for the individual patient. Robust audit trails were programmed into the system which allowed a complete record of who signed into CareWeb and when and granular information (eg, which patients were reviewed, what information was seen and for how long). An educational communication to faculty and staff provided reassurance, in the early days of the system, that employees confidentiality and privacy would be respected. A key advantage of the CareWeb system was the development of the CDR with what might be called opportunistic information integration (ie, acquisition of information from legacy systems in whatever manner was possible). For the more robust system, standards-based (eg, health level 7; HL-7) communications were used. Where much smaller systems were encountered, internet file transfer protocol batch data transfers were used. Most importantly, the user interface was designed to be as web-standard and familiar to any browser user as possible. The system was released in 1998; within the following 8 months it became the clinical information resource for the in-patient and out-patient enterprise. A survey of house staff, attending physicians, nurses, allied health, and the administrative users (more than 10,000) was performed in 1998 to evaluate why this system was adopted by users so rapidly and completely. The top reasons for user satisfaction were: (1) all clinical information was in one place, (2) ease of use, (3) familiarity of the web user interface, and (4) incremental growth of the system. Incremental growth was possible because of a remarkable synergy that developed with the medical, nursing, and administrative staff as CareWeb grew in popularity. A CareWeb steering committee (consisting of department chairman appointed representatives from each constituency) remains the main organizational body that reviews, prioritizes, and assists in the management of CareWeb development. Early data trending (tabular clinical information) was especially useful in the critical care and out-patient areas and often replaced paper flow charts. Apart from clinical department results reporting, the most clinically important component of CareWeb is the clinical documents section (see Fig. 1). Document
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5 electronic medical record safety and critical care 59 creation in CareWeb most often occurs by voice transcription; however, by the use of user-customized document templates, the create document function in the system (text file editor) is enjoying rapid growth. Recently, this component of the system was implemented in our medical ICU (MICU) and permits legible, timely daily progress notes to be edited and signed by the ICU attending and available for viewing within 15 minutes of completion. The combination of enhanced computer skills of the new generation of physicians and the value of templates that provide guides for structured medical documentation are facilitating acceptance. A particular application of this communication function is being studied in the acute care medical units for error reduction in house officer sign-outs. In our critical care units, CareWeb has continued to grow into areas beyond traditional results reporting (eg, documentation) and now exists as the primary means for clinical information editing and electronic signature. Because CareWeb is a web-based EMR system, it is accessible over the Internet and uses a Health Insurance Portability & Account ability Act of 1996 compliant double-bind password system remotely. Physicians and others access the system from offices, homes, and the field. CareWeb demonstrates why the RRIS is the key first step toward the EMR and clinical decision support in any health care enterprise. The RRIS features of improved patient information access provide the fundamental elements of decision support. With data integration and communication standards, the process of creation of a CDR is an essential first infrastructure step. It also is important to gain the trust, attention, and interest of your organization and medical staff in particular. An enterprise-wide RRIS strategy is a proven method to engage clinicians in the effective use of the electronic medical record. Computerized physician order entry in the intensive care unit There is a rapidly evolving momentum for the implementation of CPOE in American hospitals and a notion that serious medical errors can be prevented in acute care and other settings by their implementation. A recent survey of more than 900 United States hospitals demonstrates that the vast majority have not implemented CPOE systems; less than 10% reported full physician electronic order entry . The existence of this situation, despite literature that demonstrated a significant decrease in adverse drug events (ADEs) as a result of CPOE, demonstrates a conundrum of American health care that we discussed in detail . Nevertheless, the growing momentum of the regulatory, research, and political initiatives resulted in a growing awareness that a transformation in American health care, including critical care, is underway. ADEs are injuries, either preventable or nonpreventable, that are due to a drug (also called an adverse drug reaction). The literature in acute care and critical care settings demonstrates a significant impact of the CPOE component of the Fig. 1. Example of CareWeb page.
6 60 bria ii & shabot Fig. 2. The effect of CPOE on potential errors. EMR on ADEs, comparable to comprehensive MD, RN, and PharmD review teams [8,9]. An important recent report of CPOE implementation in a pediatric ICU, however, begins to refine our understanding of the value of CPOE for what it is (automation of the process of writing and transmitting orders). This well-controlled investigation examined the impact of CPOE on three aspects of order writing in the ICU: (1) ADEs, (2) medication prescription errors (MPEs; errors in which inadequate information was provided or further interpretation [eg, illegibility] was required for the order to be processed), and (3) rules violations (RVs; errors that were not compliant with standard hospital policies [eg, abbreviations]). This prospective trial was conducted on 514 pediatric patients who were admitted to a 20-bed pediatric critical care unit in a tertiarycare children s hospital before and after implementation of CPOE. A total of 13,828 medication orders was reviewed. The implementation of CPOE resulted in a nearly complete elimination of MPEs and RVs and a significant, but less dramatic, effect on potential errors (Fig. 2) . This provides a new insight into the value and limitations of CPOE. Automation of the format and communication of orders alone are only likely to impact those elements, format, and structure. These are important aspects of patient care; however, the more complex issues of appropriate care and evidence-based care rely on the implementation of more sophisticated, decision support systems. Accurate identification of errors in health care: a system issue Understanding safety: how are we really doing? As we endeavor to implement information tools to decrease ADEs and illegible medical documentation and to improve clinical decision making, the most
7 electronic medical record safety and critical care 61 fundamental question remains. How many errors are occurring in our ICUs? One of the most commonly observed limitations with implementation of clinical information systems has been this baseline comparison information that proves or refutes the value of the system in question on quality of care. That there are serious problems with what most clinicians consider to be traditional methods of error reporting provides a sobering insight. For example, although nationally reported medication errors rates vary little (from 3% to 6% overall), when automated methods of error event reporting are used, rates increased more than 81 times . These automated strategies include elements, such as recording reason for medication discontinuation, administration of antidotes or emergency clotting factor infusions, and pointing out a fundamental disconnect between what is happening in the process of care and what is codified in incident reports. Identification of errors in the critical care setting is more complicated by: (1) the acuity of the patients that are involved, (2) the plethora of data that is generated from bedside systems, laboratory studies, radiology testing, and medications administered, and (3) the challenges of integrating the input of the many health care teams that participate in the patient s care (eg, consultants, nursing, respiratory therapy, pharmacy, physical therapy). Finally, the IOM emphasized that the solution to errors in medical care is improvement in medical systems, not individuals. Although this is a noteworthy sentiment, all too often it is not the first consideration in a critical care mishap where individual responsibility, litigation fears, and risk management come rapidly to the fore. Considering the complexity of critical care patient management, it comes as no surprise that the critical care database management system is one of the most commonly implemented elements of the EMR; it demonstrates high value and little resistance. Intensive care unit device and data integration Integrated links to patient data that are generated inside and outside of the ICU are essential requirements for a successful ICU clinical information system (CIS). These links funnel clinically needed data to bedside ICU workstations (Fig. 3). With data links that are implemented fully, the CIS serves as the focal point for real-time data acquisition, validation, and display [12,13]. In early CISs, the data links frequently were designed and implemented on site by the system developers [14 16]. Complex analog and digital data links were devised for a wide variety of bedside monitoring devices. Frequently, data links to laboratory and other information-producing systems that were outside of the ICU were point-to-point connections or even derived from other devices, such as laboratory system printers. These data links were difficult to construct and maintain. Most CIS data links now can be purchased and installed as simple options to physiologic monitoring systems, bedside therapy devices, and other dataproducing systems. This was made possible by the emergence of standards for data communication between bedside devices and information systems as well as
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9 electronic medical record safety and critical care 63 for medical communications between information systems. The current bedside device communication standard is the Institute of Electrical and Electronic Engineers Medical Information Bus Standard #1073 (MIB) [17,18]. This standard defines the hardware, software, and data communications between physiologic monitors, ventilators, intravenous (IV) pumps, intra-aortic balloon pumps, and a wide variety of other devices and a CIS. The MIB provides a means to associate devices with a particular bedside, and thereby, to a specific patient in the CIS. It also permits devices to be queried on demand or at intervals or for devices to spontaneously report data and status information on an internal schedule. Most importantly, the MIB standard provides a mechanism to identify uniquely multiple similar devices at a bedside, most commonly a profusion of IV pumps around a critically ill patient. The safety advantages that bedside device interface links provide are several: Transcription errors are avoided. Bedside data are not lost if a nurse is busy with another patient. Most CISs hold raw bedside data for the nurse s review and approval when convenient. Sophisticated alarms and alerts can be devised for medication infusions to avoid overdoses. Some vendors provide MIB-like interfaces for their devices, whereas others make bedside data available as an HL-7 data stream. Either way, it is possible for most CISs to receive and to display monitoring data properly. Depending on how the CIS is configured, raw bedside data may be presented to nurses for editing, confirmation, and approval. Other kinds of devices (eg, ventilators) provide digital data that usually are not subject to editing. Such data may be displayed in the permanent chart as device data without a specific confirmation. Typically, laboratory, radiology, dictation, patient registration, and other systems communicate with CISs over HL-7 data links. HL-7 is a standard of the American National Standards Institute that defines the format and content of many different kinds of medical data transmissions. The safety advantages of data links between systems include: Prompt, error-free delivery of results Avoidance of transcription errors Automated marking of abnormal and critical laboratory values by the CIS Ability of the CIS to provide alerts to caregivers based on the data received Clinical decision support Patient safety is enhanced by effective decision support for the caregiver team. In an ICU, this includes nurses, physicians, pharmacists, respiratory therapists, Fig. 3. Intensive care unit clinical information system and interfaces.
10 64 bria ii & shabot Fig. 4. Multi-section CIS flowsheet display. and many others. How the team works in concert with the CIS is important; an effective CIS enhances collaboration between team members [19,20]. The CIS needs to support the workflow of the team on rounds as an information source and as a means to document care. A CIS can provide decision support simply by integrating data for review on screens and reports (Fig. 4). Medication management is improved with the CIS s Medication Administration Record (MAR) (Fig. 5). A CIS also can provide derived hemodynamic, respiratory, and laboratory data for decision support [21 23]. A CIS can provide context-sensitive links or infobuttons to reference information, guidelines, or related data . In addition, the CIS can provide the data for measurement of performance on the ICU core measures as proposed by the Joint Commission for Accreditation of Health Care Organizations other quality standards bodies. Critical alerting systems Introduction A growing body of evidence suggests that computerized alerting systems may improve patient safety and survival and decrease errors, the length of time patients spend in dangerous conditions, hospital length of stay (LOS), and costs. The IOM s 1999 report, To Err is Human: Building a Safe Health System, brought the problem of errors in medicine to the public s attention .
11 electronic medical record safety and critical care 65 Fig. 5. Medication administration record. Considerable debate in public and professional sectors ensued, although the precise number of injuries and deaths that are caused by medical errors remains in question. Regardless of the size of the problem, there is increasing recognition that errors in hospitalized and ambulatory patients significantly increase morbidity and mortality. Serious complications and death result from a series of events or system failures, as described in the IOM report. Not all events are obvious errors; however, if one seemingly harmless, avoidable event allows a chain of subsequent events to occur that leads to complications or death, the health care system has failed the patient. Although most hospitals have numerous policies and procedures in place to protect patients, these policies may contain holes through which errors can slip. Additionally, the results of interventions may not be delivered to caregivers in a timely fashion . Automated alerts To preclude delays, omissions, and errors in patient care, a critical alerting system was developed at Cedars-Sinai Medical Center that continuously monitors the data in a CIS for the occurrence of critical or exceptional clinical events. The alerting system makes use of all of the data that are sent to, or generated by, a CIS. Types of data that are used as input to the alerting algorithms include: Blood pressure Heart rate
12 66 bria ii & shabot Heart rhythm Pulmonary capillary wedge pressure Urinary output Ventilator data Bedside events Coma scores Presence or absence of monitoring catheters Clinical laboratory results Blood gas results Medications Medication blood levels Readmission events In addition, certain kinds of data are trended over time to determine if trend alerts should be generated. The alerting system runs on hardware that is separate from the CIS and the other systems that are shown in this diagram and uses software and rules that were written at Cedars-Sinai Medical Center. The alerting software contains a rules engine for detecting critical events and an alerting engine to notify appropriate caregivers of events as they occur. When an Clinical Lab System Blood Gas System Pharmacy System Clinical Information System Network ALERT PROCESSOR Is Data Item an Alert Key? NO Quit YES Patient Files YES Need Other Data? NO Prior and Related Data Alert Criteria Met? NO Quit YES Alert Actions Terminal Message Alert Log Wireless Alert Fig. 6. Critical alerting system.
13 electronic medical record safety and critical care 67 alert condition is detected, the alerting engine formats a message and transmits it to various recipients, based on a table of recipients per message type. Users may be notified of critical alerts in different ways, depending on the capabilities of the CIS and the urgency of the alert. For a low-priority alert, it may be sufficient to wait for a user to log on before displaying the alert. Alternatively, an message that contains the alert may be sent. For an on-line CIS, an alert message may be displayed whenever a caregiver is viewing the affected patient s record. For urgent or high-priority alerts, the message and patient ID could be displayed whenever an appropriate caregiver was logged on to any patient. For critical alerts, the most expeditious method of notifying caregivers is to push the alert message to their alphanumeric pager or other wireless device with text-receiving capabilities [26 30]. For each type of alert and caregiver, the alerting engine contains rules that determine which alerts go to which caregivers and the preferred method of delivery. The alerting engine operates by examining all the data that are sent to it. Certain kinds of data require other data items for a decision to be made about whether an alert condition is present. When necessary, the alerting engine accesses patient files to recall the needed data. When all of the data are available, the engine determines whether an alert has occurred. This determination is made using binary rules that are stored with the alerting engine. An overview of the automated decision-making process and the various methods of alerting caregivers is shown in Fig. 6. Alert inferencing strategies Certain laboratory results are appropriate for critical value alerts because severe abnormalities can lead to morbidity or mortality (Box 1). Complex trend alerts were devised for hemoglobin, hematocrit, and serum sodium with an algorithm that analyzes the amount of change between two values, the rate of change, the time span between samples, and the proximity of the most current value to a critical value limit [31,32]. Correlation of laboratory alerts with outcome Critical alerts were correlated with outcomes for patients who were admitted to the Cedars-Sinai Surgical ICU over an 8-month period. Fifteen hundred and fifteen alerts were recorded out of a total of 115,000 laboratory results that were transmitted to the CIS during the study period (alert incidence = 1.32%). Alerts were categorized as shown in Table 1. Striking differences in outcome were noted between patients who suffered one or more alerts during their stay in the surgical ICU (SICU) and those who had no alerts, as measured over a 3-month period (Table 2). These findings were confirmed in a much larger study of 3973 consecutive patients who were admitted to the SICU over the 2-year period from January 1, 1999 through December 31, These patients received 13,608 days of SICU care (Table 3).
14 68 bria ii & shabot Box 1. Laboratory results that are appropriate for critical value alerts Chemistries Sodium Potassium Chloride Calcium Hematology Hemoglobin Hematocrit White blood count Prothrombin time Partial thromboplastin time Cardiac enzymes Troponin I Arterial blood gas ph Po 2 Pco 2 Drug levels Phenytoin Theophylline Phenobarbital Quinidine Lidocaine Procainamide N-acetyl procainamide (NAPA) Digoxin Thiocyanante Gentamicin Tobramycin
15 electronic medical record safety and critical care 69 Table 1 Categories of alerts Alert type Number Percent Blood Gas % Hematology % Hemoglobin trend % Chemistry % Cardiac enzymes % Hematocrit trend % Coagulation % Drug levels: % Total: % In the latter two studies, the differences in SICU LOS, SICU mortality, and hospital mortality were highly statistically significant between patients who had no alerts versus patients who had one or more alerts. There is no question that the occurrence of even one critical alert is correlated with adverse outcomes in critically ill patients. Critical exception alerts A new form of alert was developed based on the occurrence of exceptional clinical conditions rather than laboratory values (Box 2). Exceptional clinical conditions are defined as: (1) unusual single clinical events; (2) a cluster of clinical events that occur at the same time; or (3) clinical events that occur over a period of time. Exception alert conditions are defined in a configurable, table-driven format which permits the addition of new conditions as they are appreciated. Calculation-adjusted critical alerts Certain laboratory parameters represent appropriate alert keys if other criteria are met. A common example is serum calcium, which may cause titanic muscle contractions when levels decrease to less than 7 mg/dl; however, the measurement of total serum calcium is affected by the serum albumin concentration and the blood ph. Most patients in the patients who have what seems to be critical hypocalcemia are merely hypoalbuminemic and are in no danger of tetany. The reason is that normally calcium is 55% bound to albumin; only the unbound or ionized calcium is physiologically active. Albumin binding also is affected by Table 2 Three month study-icu alerts and outcomes # patients SICU LOS SICU mortality Hospital mortality No alert conditions % 1.3% One or more alerts % 15.9%
16 70 bria ii & shabot Table 3 Twenty four month study-icu alerts and outcomes # patients SICU LOS SICU mortality Hospital mortality No alert conditions 2, % 2.0% One or more alerts 1, % 9.5% ph. Therefore, the laboratory s reported serum calcium level must be subjected to a calculation adjustment before sending caregivers a critical alert that is based on it. Whenever related laboratory values are used in calculation-adjusted limits, one must specify a time window for which the related values are appropriate to use in the calculation. Because serum albumin changes slowly in the absence of albumin infusions, it can be recognized as valid for 48 hours. Conversely, arterial ph may change rapidly; it is only considered to be valid for 2 hours for calculation adjustment purposes. Medication alerts Medications are a prime source of patient safety information. In 1998, the ICU MAR was added to the SICU CIS. All medication orders are entered into the CareVue MAR and all doses are charted in the MAR. This allowed for comprehensive medication alerts to be added to the computerized alerting system. At the time medications are ordered, the alerting system checks for allergies, crossallergies, medication interactions and medication-laboratory value alert conditions. On an on-going basis, the alerting system checks all incoming laboratory values against the ordered medications to determine if a serious condition is developing. An example would be an increasing level of serum urea nitrogen or Box 2. Currently operational exception alert conditions FiO 2 N 60% for more than 4 hours Positive end expiratory pressure N 15 cm H 2 0 Systolic blood pressure (BP) b 80 mm Hg and no pulmonary artery catheter Systolic BP b 80 mm Hg and pulmonary wedge pressure b 10 mm Hg Pulmonary wedge pressure N 22 mm Hg Urine output b 0.3 ml/kg/h and not admitted in chronic renal failure Ventricular tachycardia Ventricular fibrillation Code blue Readmission to ICU in less than 48 hours postdischarge
17 electronic medical record safety and critical care 71 creatinine while a patient was receiving a nephrotoxic drug. The system checks for increasing levels of serum urea nitrogen and creatinine in these conditions and will alert for a significant increase in the laboratory value, even if it is in the normal range. Additionally, the medication alerting system continuously evaluates bedside physiologic measurements (eg, urine output and blood pressure) and will alert if physiologic measurements indicate that an adverse drug event may be eminent. Advisory messages are transmitted to the ICU pharmacist when laboratory values related to a patient s current medications are received by the CIS (eg, a partial thromboplastin time on a patient who is receiving a heparin anticoagulant IV infusion) (Box 3). Alphanumeric alert paging Alphanumeric devices, including encrypted pagers, messaging cell phones, and other communication devices, are now widely available. A diagram of the wireless alerting system is shown in Fig. 7. Exception alerts are transmitted as soon as they are detected. Certain time oriented exception conditions are checked hourly, others are checked every 15 minutes. A typical exception alert is displayed in Fig. 8. A typical laboratory value alert is displayed in Fig. 9. Medication alerts are detected whenever an incoming medication order, laboratory result, or bedside physiologic measurement exceeds a preset medication alert threshold. Orders that are entered into the CIS s MAR are checked automatically for allergies, excessive dosage, and certain drug lab and drug drug interactions. Medication orders are checked continuously against incoming physiologic and laboratory data for evidence of adverse drug effects (eg, worsening renal function or decreasing urine output in patients who are receiving antibiotics or other drugs that are associated with nephrotoxicity). Once detected, explicit alert messages are transmitted to alphanumeric pagers that are carried by SICU residents, faculty, and the ICU pharmacist. Advisory-type messages are transmitted to the ICU pharmacist when laboratory values that are related to a patient s current medications are received by the CIS. Examples of medication alerts are shown in Figs. 10 and 11. Clinical results Execution of algorithms to transmit critically abnormal laboratory and medication results is instantaneous, whereas execution of algorithms to detect exception conditions occurs on a frequent, periodic basis. Generally, notification of exception and alert conditions is received at the pager within 1 minute of detection. Although radio transmission is subject to data traffic or other delays in the paging system, in many instances the clinician who receives the page is the first individual to be aware of, and to respond to, the life-threatening condition. This occurs despite the fact that the data item that triggered the alert was posted simultaneously to the patient s electronic chart.
18 72 bria ii & shabot Box 3. Examples of the types of medication alert conditions that are detected Allergy alerts Penicillin allergy for ordered drugs including penicillin, ampicillin, augmentin, zosyn, oxacillin, primaxin, unasyn Sulfa or Bactrim allergy for ordered drugs including Bactrim, Celebrex Any drug ordered for which an allergy is entered in the CIS Medication dosage alerts Gentamicin 200 mg Tobramycin 200 mg Vancomycin 1500 mg Phenytoin 1000 mg Digoxin 0.5 mg Heparin flush 5000 units Heparin injection 5000 units Enoxeparin 30 mg Epogen 20,000 mg Medication-physiology alerts Low urine output (b0.3 ml/kg/h for 3 hours) and the patient is on gentamicin, tobramycin, vancomycin, penicillin, ampicillin, ampicillin, augmentin, piperacillin, zosyn, oxacillin, primaxin, unasyn Medication laboratory data trend alerts Alert if serum creatinine level increases by 0.5 mg/dl in 24 hours and the patient is receiving any of the following drugs: gentamicin, tobramycin, amikacin, vancomycin, amphotericin, digoxin, procainamide, prograf, cyclosporin, ganciclovir Medication-lab advisories (sent to pharmacist only) Laboratory results sent when a patient is receiving one of the following drugs: Partial thromboplastin time on heparin drip Phenytoin level on phenytoin Prothrombin time on warfarin
19 electronic medical record safety and critical care 73 Prothrombin time international normalized ratio on warfarin Digoxin level on digoxin Gentamicin level on gentamicin Tobramycin level on tobramycin Vancomycin level on vancomycin Lidocaine level on lidocaine drip Theophylline level on theophylline Theophylline level on theodur Theophylline level on aminophylline FK506 level on tacrolimus FK506 level on prograf Procainamide level on procainamide NAPA level on procainamide Pro-NAPA level on procainamide Alert and advisory messages Wireless messages were audited for a 6-month period from July 1, 1999 to December 31, 1999 in the SICU; alerts were received on 937 patients who received 3232 days of care (Table 4). These results indicate that critical events occur in a busy, tertiary care ICU several thousand times per year. Each critical Fig. 7. Cedars-Sinai wireless alerting system.
20 74 bria ii & shabot Fig. 8. Exception condition alert: systolic blood pressure b80 mm Hg with no Swan-Ganz catheter in place. Patient identification and other information is available using the arrow scroll buttons to the right of the display. event represents an opportunity for omission, delay, or error in treatment. The potential for improving outcomes with a real-time alerting system is clear. Standards, the electronic medical record, and the intensive care unit Information systems, at the lowest level, operate on a series of 0s and 1s, the binary number system. To represent words, pictures, diagnoses, procedures, and so forth a computer system must have a number (a code for that element). Medical information has been represented in controlled dictionaries or lexicons for decades. The most commonly used lexicon in medicine is the International Classification of Diseases, Ninth Revision (and elsewhere, International Statistical Classification of Diseases, 10th Revision) system. This system was created for financial transactions rather than to communicate medical diagnoses between practicing clinicians. Internationally, many other lexicons have been developed to meet specific needs. For example, the Systematized Nomenclature of Medicine (SNOMED) lexicon initially was developed by the American College of Pathology for standardized diagnosis coding. Over the years, this lexicon has been expanded to include medications, procedures, and fine detail of clinically relevant medical information. On July 1, 2003, the Secretary of Health Fig. 9. Laboratory value alert: serum sodium (NA + ) 117 mmol/dl.
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