Information Technology in Critical Care

Chapter 8 Information Technology in Critical Care




By one analysis, medicine is an information service. Its practitioners tirelessly gather and assimilate information while sometimes adding to the collective body of knowledge. Clinical information is meticulously compiled and interpreted for each patient, the disorders that afflict them, and the therapies to treat them. Efforts to automate medicine do not place patients on conveyor belts to be serially and automatically poked and prodded. Automation efforts are directed at managing the flow of information. Medical information is wielded to protect life and to shepherd death. Compassion, judgment, and technical skill may distinguish excellence in the discipline, but information defines medicine.


The volume of medical information, expanding at fantastic rates, threatens to drown even the most conscientious practitioner who devotes every waking hour of every day to collecting, cataloguing, and assimilating it. Advances in information technology (IT) can both fuel the information explosion and contain it. Computers connected to one another and to large data repositories give practitioners immediate access to vast knowledge and data while streamlining the tedious chores of searching and collating that information. IT has changed the way we practice.


Demonstrations of the potential of IT typically inspire awe and admiration. However, when the technology migrates from demonstration to actual use, awe and admiration sometimes give way to disappointment and disgust. The novel features that users think they need are either impossible to achieve or require significant reengineering of the original product. The lure of the idealized technology suffers from its real limitations. Knowing the limitations helps users to avoid falling victim to them. This understanding can help the clinician focus on what can be accomplished readily while awaiting “the next upgrade.”



The Electronic Health Record


The computer-based patient record (CPR) or electronic health record (EHR) is defined as a comprehensive database of personal, health-related information that is accessed and updated across a health care network.1 Its potential and real benefits include the following:






The three principal functions of such a database, like any database, are data acquisition, data access, and data storage.



Data Acquisition


The complete EHR acquires data from a variety of sources, including hospital registration, nursing and physician input, laboratory services, radiology and other test interpretations, therapist and nutrition services, monitoring devices, and physicians’ orders. The most important system that feeds the database is the “enterprise-wide master patient index,” which ensures that each patient is identified properly and uniquely. All other systems must have the correct identifier in order to deliver their data to the correct patient record. A multimedia database can include images such as radiographs, electrocardiograms, fetal monitoring, sonography, magnetic resonance images, computerized tomograms, and even paper-based documents such as consent forms, questionnaires, and, sometimes, handwritten notes and hand-drawn diagrams. Data acquisition is organized in a manner that minimizes duplicative effort and maximizes data consistency.2


One of the significant challenges to any implementation of an EHR is engineering the various interfaces between it and the host of systems that feed it data. Some of the feeder systems, such as laboratory services, have their own established validation protocols that are applied before transmission to the EHR.


Data originating from bedside devices such as cardiopulmonary monitors, pulse oximeters, ventilators, and intravenous infusion pumps represent critical elements of the patient care record. Manual capture and entry of these data into the EHR by nurses and other healthcare practitioners is associated with inefficiency and transcription errors. Technology is currently available to connect devices to the EHR through bedside medical device interfaces (BMDI). BMDI allows properly formatted data from a medical device to flow into and update the patient’s EHR. One of the challenges in BMDI relates to the diversity of medical devices and EHRs, which makes it impractical for most vendors to directly connect. Often, biomedical device integration systems are required to extract, read, interpret, and forward data to the EHR in order for it to be useful. Basic physiologic data including heart rate, blood pressure, and respiratory rate are generally the first to be targeted for EHR integration. Other types of monitoring data that can be integrated with the EHR include temperature, pulse oximetry, end-tidal CO2, and cardiac output measurements. Other types of devices that can be utilized for BMDI connectivity include intravenous infusion pumps, ventilators, dialysis, hemofiltration systems, cerebral oxygenation monitors, and extracorporeal membrane oxygenation systems.


One of the clear benefits associated with BMDI is the improved efficiency associated with not having to manually retrieve and record the data. The increased efficiency therefore allows the nurse to spend more time at the patient’s bedside or in other important activities. Though this time saving is minimal for any one piece of patient data, when the aggregated data for each patient and for the entire facility is considered, the opportunity for cost saving likely exceeds the cost of the initial investment. Another potential benefit concerns the completeness and accuracy of the data in the EHR. BMDI increases data-sampling frequency possibilities, which is particularly important in a dynamic patient situation where data may be changing rapidly. Caretakers may request higher data sampling frequencies without impacting the need for additional patient care resources. In addition, reading and manually recording data into an EHR is often associated with transcription errors. BMDI reduces the likelihood of these transcription errors.


Considering that a data element passes from one of several feeder systems through different computers, with possible transformations of that data element along the way, and considering the possibilities of lost transmissions, computer down-times, and network interruptions, consistent error-free data feeding would seem a virtual miracle. In a high-volume environment, a centralized interface engine that routes and converts transaction messages from disparate feeder systems can solve many of the interface issues efficiently and in a timely fashion.


The capture of textual information, such as progress notes, nursing assessments, or even radiology reports, presents particular challenges for several reasons. For the most part, text is entered via a keyboard, but alternatives include voice recognition, handwriting recognition, or handheld and wireless devices. Many other technologies have failed in practice to date. Semiautomated text entry with menu systems feeding structured and unstructured forms have met with some success. Although these solutions do not have the same expressivity of free text, they lend themselves to the capture of text as data. Collecting data better allows for future analysis, but despite this significant advantage over collecting bland text, it tends to be rigid, can make documenting the unusual impossible, generally requires more time to collect, and may be a significant source of frustration for the clinician. An as-yet-untested strategy is to allow free text entries but to apply natural language processing to extract data from it for analysis. The decision to pursue data rather than text requires an institutional commitment to the philosophy that data are more valuable and are worth the difficulties they can present.



Data Access


The computerized patient record serves as the focal point for most health care professionals. It might be accessed at inpatient sites, but also in emergency departments, nursing facilities, continuing care centers, physician offices, clinics, laboratory facilities, treatment centers, and, in the case of home health services, the patient’s home. An ideal computerized patient record should be available when and where it is needed. However, databases with sensitive information must be controlled to prevent unauthorized use or alteration. These systems must satisfy five requirements:







The interface through which most health care providers interact with the EHR should be user-friendly and intuitive. Most clinicians have little time or patience to sit through tedious training sessions, and, once trained, few clinicians will recall more than a minimum required to complete their immediate, routine tasks.


The system should be capable of providing a full, seamless view of the patient over time and across points of care. Views should be configurable so that a given user’s information needs and workflow can be accommodated. Both detailed and summary views that juxtapose relevant data allow the clinician to acquire the information required to optimize expedient decision making. Displays should be configured to highlight key information while suppressing clutter but making all pertinent data readily accessible. Dynamic linkages should exist between the computerized patient record and supporting functions such as expert systems, clinical pathways, protocols, policies, reference material, and the medical literature.


Response times must be sufficiently speedy and workstations should be conveniently accessible to the point of care. Mobile connections are a bonus. Access to patient data via wireless connections with portable devices is an attractive alternative for users but must overcome usability and security hurdles before it can be fully implemented (see section on Security & Privacy: Wireless Networks).


The patient database also supports many areas of research, education, decision support, and external reporting. Thus, data in aggregate can be accessed by administration, finance, quality assurance, and research areas.



Data Storage


The multimedia data of the comprehensive EHR are stored on media that allow for long-term storage while allowing searches and rapid retrieval of enormous volumes of data. The database must be updated in a way that ensures that it is current, complete, and consistent. Data, once entered, should be modifiable only in accordance with strict rules that assure data integrity.


The architecture of the database can be centralized or distributed, replicated or not. A centralized database is stored at a single site, whereas a distributed database is a single logical database with segments that are spread across multiple locations connected by a network. A replicated database has the advantage over a nonreplicated database by having at least one copy of all records in case the primary copy is inaccessible because of computer or network failure. The challenge of replication is maintaining consistency among all the copies, requiring timely, automatic synchronization of the original database and its replicas.


Even replicated databases must be backed up periodically to ensure against data loss. It is essential for an EHR to have a strategy for doing so as seamlessly as possible and for establishing a clear and workable recovery.


Once stored, the data should have a time stamp. Although the data can be modified, both the original and the revised versions should be maintained with appropriate time stamping. Appropriate safeguards must ensure database integrity so that its pieces do not lose their links and that the data are not subject to unauthorized modification. Supplanting the paper record with the EHR as the official medical record requires thoughtful consideration of the limitations of paper copies to reflect accurately the electronic record. Sanctioned hard copies of the patient record will be necessary for sharing with other health care institutions or with the legal system.


Whereas a clinical data repository is a database optimized to retrieve data on individual patients, a data warehouse is a database designed to support data analysis across individuals. This function can be distinguished from a simple archival function. The warehouse structure is designed to support a variety of analyses, including elaborate queries on large amounts of data. The data are generally static and updated intermittently in batches rather than continuously.


Hospitals can use data warehouses to perform financial analyses or quality assessments. With decision support tools, they can be useful in negotiating managed care contracts or distributing resources to clinical or ancillary services. Subsets of a data warehouse that are structured to support a single department or function are “data marts.” These subsets are designed to perform periodic analyses or to produce standard reports run repeatedly, such as monthly financial statements or quality measures. Online analytical processing (OLAP) is decision support on databases that are partially digested for analysis and thus are more rapidly accessed. In finance and administration, they can assist in strategic planning by predicting the impact of decisions before they are made. In medicine, it can take the form of a clinical database to support evidence-based decisions. Data mining applications can sift through mountains of data in the warehouse and run complex algorithms to find obscure patterns. However, as with any database, the questions must be defined as precisely as possible and the database designed accordingly if meaningful results are to be expected.



Clinical Decision Support


Decision support systems are an integrated set of programs and databases that provide users with the ability to interrogate those databases and analyze information, retrieving data from external sources, if necessary, to assist in decision making.3


Most medical decision support systems are designed to improve the process and the outcome of clinical decision making. They can yield most of the benefit of clinical information systems; for example, they can shorten inpatient length of stay, decrease adverse drug interactions, improve the consistency and content of medical records, improve continuity of care and follow-up, and reduce practice variation.


Retrospective decision support tools can be applied to aggregate patient data to find historical patterns. Real-time decision support systems can be passive or active. Passive systems are activated when clinicians request help. Such assistance can come as reference material, automated calculations, or data review. Active systems include alerts and reminders that are triggered by preprogrammed rules governing specific circumstances. For example, an order for penicillin in a patient who is allergic to it can cause a warning to display.


An effective decision support system must have accurate data, a user-friendly interface, a reliable knowledge base, and a good inferencing mechanism. The knowledge base can include information regarding risks, costs, disease states, clinical and laboratory findings, and clinical guidelines. The inference engine determines how and when to apply the appropriate knowledge while carefully minimizing disruptions of workflow.4,5



Patient Safety


Patient safety concerns remain paramount in any hospital system, including clinical information systems.6,7


To the extent possible, redundant systems should be in place to minimize the effect of the failure of a single component. Robust down-time contingency plans must be developed should the clinical information systems cease normal function in either planned or unplanned situations. These contingency plans must account for continued data acquisition and retrieval and provide for mechanisms for communication among health care providers and services. Users should be informed about recovery procedures and what they mean to the clinical database. Do backlogged data generated during the down time ever enter the system? How are they timed? Or is there a gap in the clinical information that the clinicians must fill in for themselves if they want the whole picture?


Many anomalous circumstances related to the EHR can threaten patient safety. Data, such as a laboratory value or a physician order, can be entered into the wrong patient record and prompt the clinician to respond appropriately but on the wrong patient. Similarly, data can be displayed in ways that are so confusing that they are interpreted incorrectly.


Default behaviors of portions of the computerized patient record should be designed carefully, because busy or distracted clinicians may accept the default without understanding what they are accepting or without considering the consequences.



Automated Adverse Event Detection


Children are at significant risk for adverse drug events, and recent studies have begun to describe the frequency and epidemiology of medication errors and adverse events in pediatric inpatients.810 In 2006, The Institute of Medicine released guidelines urging improved surveillance systems to detect adverse events.11 Traditional methods used to detect adverse events in children included manual chart review and voluntary incident reporting. These detection systems are inefficient and significantly underestimate the number and prevalence of adverse events.12,13


Another manual detection strategy relies on trigger methodology where an occurrence, found on manual chart review, triggers further investigation to determine the presence of an adverse event.11,14 For example, the administration of flumazenil may trigger the detection of benzodiazepine-induced respiratory depression. Automated adverse-event detection relies on the generation of a trigger report from the EHR, which indicates the possibility that an adverse event has occurred requiring further investigation. This methodology has been proven an efficient and cost effective way to detect adverse events.1519



Promises and Limitations


Information technology in the form of an EHR promises improved patient care.20,21 Potential benefits of information technology include providing rapid access to integrated clinical data and extant medical knowledge, eliminating illegibility, improving communication, and issuing applicable reminders and checks for appropriate medical actions.22


A number of studies show that information technology can provide various benefits, including increasing adherence to guidelines (particularly in the outpatient arena) and decreasing some medication errors.23,24 However, the majority of these studies come from a very small number of institutions with homegrown clinical information systems that were developed by devoted groups of clinicians.25 Very few studies show that the commercially available systems confer similar benefits, and even if they do, it is unclear that their success can be migrated from one implementation to another.2628 In fact, any benefit may be outweighed by new problems introduced by the systems themselves. In effect, one set of problems may be traded for another.29,30


Despite considerable progress, the sentiment expressed by G. Octo Barnett in 1966 is often echoed today, “It is frustrating to meet with repeated disappointments when the objectives are superficially so simple.”31 The medical information space is vastly more complicated than it seems at first. EHR software programs are enormously complex, are built by large teams of programmers with input by numerous clinicians, demand high-speed processors and high-bandwidth networks, and rely on often fragile interfaces with other hospital systems. Implementation currently requires tremendous effort by both clinicians and technical specialists to configure these systems according to the specific needs of an institution and in ways that will enhance care rather than impede it. An often unappreciated complicating factor is that the technology does not simply replace paper; it also reengineers care—deliberately or not. (See Unfavorable Alteration of Workflow.)


Errors can and do occur in programming or configuration. Many programming deficiencies can be detected and corrected with thorough testing, preferably in a development environment that does not affect real patients; however, some of these problems will only become apparent under unique circumstances that are presented by patient care. Indefatigable vigilance for these errors is essential.


Numerous other unintended consequences result from implementing an EHR, including the creation of new kinds of errors, an increase in work for clinicians, an untoward alteration of workflow and change in communication patterns, an increase in system demands, a continuation of the persistence of paper use, and the fostering of potential overdependence on the technology.3234



New Kinds of Errors


While some errors can be avoided by using an EHR with computerized physician order entry (CPOE), other errors may be created or propagated.35,36 Many “new” errors are a result of poorly designed interfaces. For example, clinicians can easily make “juxtaposition errors,” intending to select one item but selecting another close to it on a long, dense pick list in a small font. A similar kind of error is mistaking an open chart of one patient to be that belonging to another or picking the wrong patient from a long list of patients.


Interfaces between electronic systems are particularly vulnerable and can cause various new kinds of errors. Patients who have been physically transferred but remain, disembodied, in their previous electronic location may have all of their care suspended pending completion of the electronic transfer. Worse, should electronic transfers be delayed, medications may be delivered to a patient’s former room and administered to a different patient admitted to that room. Allergies may be entered in the bedside system, but interface problems can prevent that information from reaching the pharmacy or nutrition systems. Occasionally, laboratory results can be inserted into the wrong medical record because of interface issues.


Rigid interpretation of policies and procedures can be configured into the EHR but may lead to difficulties in clinical practice when dealing with ambiguous circumstances and exceptions. Sometimes the process of care is incompletely understood and codification can be disastrous. Policies at most institutions include automatic stop orders that require rewriting medication orders in a specified time frame. Compliance to this rule can be forced with programming, but implementing this rule without safeguards could lead to automatic discontinuation of medications and missed doses.


The benefit of legibility in electronically written notes can be outweighed by novel problems. Overuse of copy-paste functions can result in repetitive, monotonous, and loquacious notes punctuated by the sin of propagating erroneous text verbatim. Automatic transcription of data such as laboratory results or vital signs often bypasses cognition, something that does not happen when data are transcribed by hand.



Increased Work for Clinicians


While transcription errors can be eliminated by computerized order entry, it often falls to clinicians to shoulder the added burden of what might otherwise be considered clerical functions. Documentation in a structured format rather than as free text can enhance completeness and facilitate later data retrieval; however, it can also increase work by forcing the clinician to find ways to fit round pegs into square holes. Similarly, rigidly structured order input can force clinicians to waste time trying different ways to order nonstandard tests or therapies—with little guarantee that these orders will actually be executed if they are routed to electronic limbo.


Clinical alerts can help clinicians make decisions, e.g., when penicillin is mistakenly ordered for an allergic patient, but persistent interruptions of work by alerts can increase the workload of the clinician who must decipher their meaning and assess the risk in each specific circumstance. The frequency of these alerts can become intolerable when they are not delivered to the right clinician with the right information and at the right time and place. When these alerts become too frequent and too predictable, clinicians often adapt by “response chaining”: dismissing the alerts with rote keystrokes much as a pianist plays a familiar tune. Alerts that evoke this response cannot be effective and may be counterproductive.37


Poorly integrated clinical information systems cause clinicians to access many different sources for information to solve a clinical problem, thereby increasing work. Similarly, users should not be required to input the same bit of data in multiple locations in different systems.


Another time-consuming feature of the EHR, and perhaps the most exasperating, is the loss of data, particularly when busy clinicians lose long notes they have just meticulously written. Workstation or interface crashes, network collisions, inopportune time-outs, or system failures of other types can be the culprit. System delays from a wide variety of causes also waste valuable time, as does having to hunt for an available workstation because those installed are insufficient in number or inconveniently placed.



Unfavorable Alteration of Workflow


The introduction of an EHR system significantly alters the sociotechnological milieu. Previously well-functioning medical practices may become entirely dysfunctional. Implementation of an EHR requires modeling of work processes but can sometimes result in ossifying those processes into something too inflexible for efficient and effective patient care.


Patients expected to be emergently admitted to the pediatric intensive care unit (PICU) but still in transit often have medications and urgent therapies ordered and prepared before arrival. A CPOE system may prohibit ordering or dispensing medications for patients who have not yet been admitted. In a paper environment, nurses frequently arrange dosing schedules based on the ordered frequency, the frequency of other ordered medications, and the availability of intravenous access. However, in many CPOE systems, medication orders go direct to the pharmacy and bypass the bedside nurse. Physicians are then saddled with picking the specific times of administration, only to have that schedule revised later by the nurse.


Sometimes, well-defined manual processes can be implemented in more than one way electronically. Without clear delineation of an institutionally sanctioned method, confusion and catastrophe can result. Transferring patients from one unit to another or to the operating room typically requires the discontinuation and reinitiation of all orders, including medications. With implementation of CPOE, clinicians without proper direction could suspend rather than discontinue the old orders. Reactivation of suspended orders could result in duplication and double dosing of medications ordered on transfer.


Redundant orders are sometimes facilitated by CPOE. The gate-keeping function of clerical personnel processing orders for routine radiographs or laboratories is bypassed. Remote access by multiple physicians acting on the same bit of new information can also generate duplicate orders.







Human Factors Engineering


Cognitive science, computer science, and human factors engineering are among many disciplines that can facilitate development of a successful EHR system. Human factors engineering investigates human capabilities and limitations and applies that knowledge in the design of systems, software, environments, training, and personnel management. Application of human factors considerations in developing an EHR, particularly regarding CPOE, can maximize successful design and implementation of these systems. Some human factors principles may seem self-evident but can be overlooked when not approached systematically. Developers must understand the users, undertake detailed task analyses, and assess computer-supported cooperative work—the study of how people work within organizations and how technology affects them and their work. Three principles that may improve clinical information systems are accounting for incentive structures, understanding workflow, and promoting awareness of the activities of other group members. Institutional and personal incentives for using an EHR differ, but only the latter will effectively influence use. Awareness of the roles played by other team members enhances collaboration. Improving collaboration may decrease the incidence of medical errors.39,40


Another important area of human factors engineering relates to interface design. Interfaces should be simple and consistent, with important data highlighted, such as the patient name or weight. “Progressive disclosure” means that commonly used and important functions should be presented first and in a logical order, whereas infrequently used functions should be hidden but available. Minimizing “human memory load” can be accomplished by displaying all relevant information together on one screen rather than relying on the user to remember critical bits of data from different parts of the chart. Potential user errors should be anticipated, and easy error recovery should be designed into the system. Error messages should be informative and could include advice about error recovery. Other feedback should be provided to acknowledge user actions, particularly when the system appears frozen. Given the chaotic healthcare environment, the interface should also be designed to forgive interruptions, allowing work to be saved and facilitating task resumption.


User satisfaction is an important predictor of system success. Satisfaction is enhanced when the systems are designed with the users’ needs and preferences in mind. Peers who serve as advocates for their groups during development and subsequently teach other users generally increase acceptance of the systems. Ease of use, rapid response times, flexibility and customizability, mobile workstations, implementation of effective decision support tools, access to reference information, and adequate training and support are all important factors in enhancing both user satisfaction and system success.41



Continued Promise


The Institute of Medicine, in its report, “Crossing the quality chasm: a new health system for the 21st century,”2 stated that health care should be safe, effective, patient-centered, timely, efficient, and equitable. The Institute further noted that these goals could be more easily reached through judicious application of IT. Automated order entry systems can improve safety. The use of automated reminders based on clinical practice guidelines, computer-assisted diagnosis or management, and evidence-based medicine (EBM) can improve the effectiveness of medical care. IT can enhance patient-centered care that is respectful of and responsive to patient preferences, needs, and values by recording them and appropriately reminding the health care professional. It can facilitate access to clinical knowledge through web sites and online support groups. Clinical decision-support systems can be used to tailor information and disease management messages based on the patient’s individual needs. Timeliness can be improved by e-mail, telemedicine, and direct and immediate access to diagnostic test results and other clinical information. IT can improve efficiency by using clinical decision-support systems to reduce redundant and unnecessary tests and procedures, by improving communication among multiple providers of care to individual patients, and by supplying data for performance and outcome measures. Enhancing equity among patients and across socioeconomic, geographic, race, and ethnic lines can be achieved if IT can improve access to clinicians and clinical knowledge, although it would depend upon equitable access to the technology infrastructure. IT is playing the starring role in the drive to improve the quality of health care today, and the Institute called for a national commitment to build the information infrastructure to support health care.



Design and Implementation


Implementation of an EHR system requires an investment of additional staff, hardware, software, and an expanded communications infrastructure or network. For large hospital networks, the costs can be exorbitant.42


Developing an EHR requires careful planning and phased implementation. The specific needs of the institution must be examined, particularly with regard to the existing technology and practices. The process should be viewed as an opportunity to enhance care, rather than simply to replace the paper, and requires reassessment of existing practices and re-engineering of healthcare delivery. As each incremental phase of implementation is approached, the focus should be on overcoming specific barriers to care rather than on the nebulous goal of “creating a paperless process.”43


The first phase generally provides a patient-centric repository of clinical test results, including laboratory, radiology, pathology, and other textual data. A subsequent phase can include capture of paper document images, radiology images, and other nontextual data. A key phase is the capture of clinical data at the point of care, including vital signs, intake and output, nursing documentation, and physician notes. Implementation of a physician order-entry system is another key phase that requires careful coordination among services and interdigitating systems.4446


Ensuring that the EHR satisfies every need involves considerable planning, designing, and testing. Even well-designed, off-the-shelf EHR systems can satisfy only 80% of the complex requirements of any multipractitioner organization. The remainder must be either adapted from other content or created from scratch. Substantial “expert” direction from teams of physicians, nurses, other allied healthcare providers, and medical records and financial staff is required to assist in developing the design and implementation of all EHRs.47 If clinicians abdicate their responsibility in participating in this tedious process, they are virtually ensuring that the resulting system will fail to satisfy their needs. Physician acceptance and participation can be enhanced by acknowledging the importance of physicians in the process, training them early and often, frequently and routinely eliciting their feedback, and demonstrating responsiveness to their needs and concerns.


Clinical information technology specialists generally interpret the requests from clinicians for configuration. A dedicated technical staff must also ensure instantaneous access to, and constant availability of, patient information. As the size and variety of information systems increase, enterprises will find it necessary to implement a “help desk” service.

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Jul 7, 2016 | Posted by in CRITICAL CARE | Comments Off on Information Technology in Critical Care

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