Computers in Critical Care

Creating an oversight infrastructure for electronic health record-related patient safety hazards

Electronic health records (EHRs) have potential quality and safety benefits. However, reports of EHR-related safety hazards are now emerging. The Office of the National Coordinator for Health Information Technology recently sponsored an Institute of Medicine committee to evaluate how health information technology use affects patient safety. In this article, we propose the creation of a national EHR oversight program to provide dedicated surveillance of EHR-related safety hazards and to promote learning from identified errors, close calls, and adverse events. The program calls for data gathering, investigation/analysis, and regulatory components. The first 2 functions will depend on institution-level EHR safety committees that will investigate all known EHR-related adverse events and near-misses and report them nationally using standardized methods. These committees should also perform routine safety self-assessments to proactively identify new risks. Nationally, we propose the long-term creation of a centralized, nonpartisan board with an appropriate legal and regulatory infrastructure to ensure the safety of EHRs. We discuss the rationale of the proposed oversight program and its potential organizational components and functions. These include mechanisms for robust data collection and analyses of all safety concerns using multiple methods that extend beyond reporting, multidisciplinary investigation of selected high-risk safety events, and enhanced coordination with other national agencies to facilitate broad dissemination of hazards information. Implementation of this proposed infrastructure can facilitate identification of EHR-related adverse events and errors and potentially create a safer and more effective EHR-based health care delivery system.

Computers in the ICU: where we started and where we are now

The first use of computers in critical care units were described in the mid 1960s. They reported the use of very large mainframe computers that filled entire rooms yet had very limited memory and processing capacities by today's standards. These were limited to only a few institutions until microprocessors were developed increasing computation speed and expanding memory capacity by many magnitudes. This allowed smaller more affordable stand alone systems to be developed and the inclusion of microprocessors into bedside devices. As the capacity expanded uses broadened. Simple results review developed into a more complete electronic medical record. Databases were created allowing population analysis for research and systems quality improvement activities. Decision support started as simple alerting of potential errors and dangers and expanded into more sophisticated clinical decision-making support. With this came problems that needed solutions. As the amount of information became overwhelming to the bedside clinician, methods to filter and display data made it more useful. Security and confidentiality became major concerns. Data input solutions had to be found including interfaces between computers, bedside devices and instruments designed to automate data input like scanners, bar coders, and other devices. The biggest issue of all however, was developing acceptance among clinicians and creating the cultural change required for successful implementation of electronic medical records. This paper will explore these issues.

Electronic medical record in the intensive care unit

The EMR in the ICU has the utility of providing the necessary information to make sound clinical decisions for critically ill patients. For it to be optimized, the EMR must be more than just what is being replicated in the written record or merely a documentation tool; it must add value that supports and enhances clinical decision support. The EMR is too expensive a tool just to be a computer designed to ease documentation and retrieve data faster. Gardner and Huff have suggested that the EMR must answer three questions: Why, What, and So What. The "Why" is relatively easy to answer, but the "What" data to use so that the information is meaningful to a provider and the "So What" are more difficult to answer. Provided one can qualitatively assess "What" information is important for a health care provider, then "So What" becomes an important objective in the empirical quantification of the benefits that the EMR provides. It is clear that to analyze some of the outcomes that health care delivery provides, one needs some mechanism to automate the information at the point of care, particularly now that the regulatory agencies are requiring it. Given the fact that there is no single integrated computerized patient record, this becomes the daunting task for the next century. Making it easier for health care providers to interact with the system and providing them with instantaneous feedback that changes their medical decision so they can deliver better care (clinical pathways, clinical practice guidelines) will be the task required of the next generation of CISs.

Computerized ventilator data selection: artifact rejection and data reduction

OBJECTIVE

To determine acceptable strategies for automated data acquisition and artifact rejection from computerized ventilators using the Medical Information Bus.

DESIGN

Medical practitioners were surveyed to establish 'clinically important' ventilator events. A prospective study involving frequent data collection from ventilators was also conducted.

SUBJECTS

Data from 10 adult patients were collected every 10 seconds from a Puritan Bennett 7200A ventilator for a total of 617.1 hours.

INTERVENTIONS

Twelve different computerized data selection and artifact algorithms were tested and evaluated.

MEASUREMENTS AND MAIN RESULTS

Data derived from 12 data selection algorithms were compared with each other and with data manually charted by respiratory therapists into a computerized charting system. Ventilator setting data collected by the algorithms, such as FIO2, reduced the amount of data collected to about 25% compared to manually charted data. The amount of data collected for measured parameters, such as tidal volume, from the ventilator had large variability and many artifacts. Automated data capture and selection generally increased the amount of data collected compared to manual charting, for example for the 3 minute median the increase was a modest 1.2 times.

CONCLUSION

Computerized methods for collecting ventilator setting data were relatively straightforward and more-efficient than manual methods. However, the method for automated selection and presentation of observed measured parameters is much more difficult. Based on the findings and analysis presented here, the authors recommend recording ventilator setting data after they have existed for three minutes and measured parameters using a three minute median data selection strategy. Such an algorithm rejected most artifacts, required minimal computational time, had minimal time-delay, and provided clinically acceptable data acquisition. The results presented here are but a starting point in developing automated ventilator data selection strategies.

Perspectives on development of IEEE 1073: the Medical Information Bus (MIB) standard

Automated data capture from bedside patient medical devices is now possible using a new Institute of Electrical and Electronic Engineering (IEEE) and American National Standards Institute (ANSI) Medical Information Bus (MIB) data communications standard (IEEE 1073). The first two standard documents, IEEE 1073.3.1 (Transportation Profile) and IEEE 1073.4.1 (Physical Layer), define the hardware protocol for bedside device communications. With the above noted IEEE MIB standards in place, hospitals can now start designing customized applications for acquiring data from bedside devices such as bedside monitors, i.v. pumps, ventilators, etc. for multiple purposes. The hardware 'plug and play' features of the MIB will enable nurses and physicians to establish communications with these devices simply and conveniently by plugging them into a bedside data connector. No other action will be necessary to establish identification of the device or communications with the device. Presently to connect bedside devices, technical help from hardware and software experts are required to establish such communications links. As a result of standardization of communications, it will be easy to establish a highly mobile network of bedside devices and more promptly and efficiently collect patient related data. Collection of data automatically should lead to the design of new medical computing applications that will tie in directly with the emerging mission and operations of hospitals. The MIB will permit acquisition of patient data more efficiently with greater accuracy, more completeness and more promptly. The above noted features are all essential to the development of computerized treatment protocols and should lead to improved quality of patient care. This manuscript provides the rational and historical overview of the development of the MIB standard.

Clinical Information Systems for Intensive Care, Pediatric Critical Care, and Neonatology

Computer information systems are expected to soon take the place of current paper charting practices, and they offer great promise to assist management of the considerable amounts of data encountered in the information-rich environment of intensive care units (ICUs). Efforts to create an electronic medical record (EMR) have been underway for more than two decades, and major national organizations, such as the Institute of Medicine, have issued recommendations on standards. Benefits of an EMR include a legible patient record, enhanced communication, provision of timely reminders and alerts to clinicians, reduction of calculation errors, access to data bases for quality assurance and research, reduced healthcare costs, and improved patient outcomes. Despite these benefits, successful EMR implementations have been confined to a few committed institutions, and expensive failures have occurred. Practitioners of neonatology and pediatric intensive care are likely to have substantial difficulty implementing an EMR to fit their specialized needs because most experience in this area has been gained through care of adult patients, and systems being developed are oriented toward nonpediatric patients. It is therefore important to examine experience thus far with the functional components of an EMR so practitioners will be able to evaluate systems better as they become available. System components discussed include nursing charting facilities, lab reporting, physician order entry, physician progress notes, structured reports, decision support systems, and problem list management. Other concerns discussed include research and quality assurance functions, data access and confidentiality issues, and electronic mail. Maximizing the “structured data” content, as opposed to narrative content of an EMR, is an important priority, and progress on developing a uniform medical language is discussed. An approach to evaluating clinical information systems for use in the ICU is presented; it should assist practitioners of pediatric critical care and neonatology in identifying computer-based charting solutions that are optimal for infants and children, while cooperating with medical center-wide needs for compatibility and a common data base.