Beverley J. Leyerle

The PDMS as a focal point for distributed patient data

SummaryDistributed data links are essential requirements for a successful patient data management, system (PDMS). These links funnel clinically needed data into bedside ICU workstations. We have constructed four data links which acquire most of the objective data required for direct patient care. Data from bedside monitors is acquired via a standard HP Signal Distribution Network. Urine volumes and core bladder temperatures are acquired over a link to 20 electronic urimeters. Clinical laboratory data is obtained over an HP General Purpose Data Link (GPDL) to a VAX 11/785 laboratory system. Blood gas data is obtained over a second GPDL link to a DEC 11/23 computer. The ICU staff is notified of incoming lab results with bedside video messages. Combined with automated calculations, these data links eliminate thousands of data entry keystrokes daily and allow the PDMS to serve as the focal point for real-time patient care.

Decision support alerts for clinical laboratory and blood gas data.

SummaryWe have designed and implemented a computerized Intensive Care Unit (ICU) decision support alerting system which analyzes all incoming laboratory and blood gas data for critically abnormal values and trends. A computerized patient data management system (HP 78709A PDMS, Hewlett-Packard Co., Waltham, MA) serving 20 Surgical ICU beds is networked to a Clinical Laboratory Information System and a blood gas computer system. The ALERTS subsystem operates on the PDMS as an automatic program triggered by the receipt of fresh laboratory data. Three types of ALERTS are detected: (1) high and low critical values, (2) calculation-adjusted critical values, and (3) critical trends. Once detected, a specific ALERT message is displayed at the bottom of the patient’s bedside PDMS terminal and at the central station.Over an eight month period a total of 1,515 ALERTS were detected from amongst approximately 115,000 laboratory data results transmitted to the Surgical Intensive Care Unit (SICU). Slightly over half of all ALERTS were caused by critical blood gas values. ALERTS were found to be a sensitive indicator of severity of illness: patients with one or more ALERTS suffered an ICU mortality of 9.52%, compared to 0% mortality in patients with no ALERTS. We conclude that automated laboratory data ALERTS represent a valuable decision support tool for the management of high risk ICU patients.

Automatic extraction of intensity-intervention scores from a computerized surgical intensive care unit flowsheet

Systems that objectively score severity of illness and intensity of patient care interventions have been used to guide the appropriate use of intensive care facilities, provide information on nurse staffing ratios, validate subjective classifications of patient illness, and normalize scientific and financial studies for severity of illness. Existing scoring systems require a well-trained observer to perform a thorough chart review to complete manual scoring forms. We have designed a new system in which computerized intensity-intervention scores are automatically extracted from electronic intensive care unit flowsheets, eliminating both manual labor and potential observer variation. In prospective studies, these computerized scores correlated well with manual TISS scores, intensive care unit mortality, intensive care unit length of stay, hospital length of stay, and a subjective classification of patients to graded levels of hospital care. Such automated scores may be used for real-time allocation of health care resources and normalization of prospective studies for severity of illness.

Inferencing Strategies for Automated Alerts on Critically Abnormal Laboratory and Blood Gas Data

A relatively insignificant amount of human thought is required to recognize critically abnormal events. After a few weeks of training on the ward, most medical students can recognize seriously abnormal results of common laboratory tests and take some definitive action, such as calling a supervising physician. The “gestalt” by which laboratory results are appreciated as clinically dangerous is complex and challenging to duplicate in a modern digital computer.

Integrated computerized databases for medical data management beyond the bedside.

SummaryComputers are beginning to be utilized extensively for direct patient care, assisting nursing and medical staff with data collection and review at the bedside. However, most clinical data management systems are optimized for bedside patient care and offer limited resources for multi-patient data analysis. At Cedars-Sinai Medical Center, a network of computer systems has been developed to provide linkages between clinical, administrative and outcome data for Surgical Intensive Care Unit (SICU) patients. Increasingly, such data is needed to evaluate the relationship between severity of illness and patient outcome and the utilization of expensive critical resources.Over the parts 3 years, comprehensive data on 6,755 consecutive SICU patients receiving 18,394 days of care have been accumulated by our PDMS. Using linkages constructed to other hospital systems and databases, trends for severity of illness, severity adjusted survival, census, bed utilization, nursing utilization and many other parameters have been constructed. These linkages are valuable in documenting cost-effective and medically-effective patient care practices.

An automatic PDMS interface for the Urotrack Plus 220 urimeter

SummaryPeriodic measurements of urine volume and temperature in critically ill patients are time consuming, unclean procedures. These measurements may be automated with an electronic urimeter (Urotrack Plus 220, C.R. Bard Company, Murray Hill, NJ). This device contains an RS-232 output port which transmits a complete device status report once per second. We interfaced 20 Urotrack Plus urimeters to a single I/O port of a computerized patient data management system (Hewlett-Packard 78709A PDMS, Hewlett-Packard Company, Waltham, MA). This interface required daisy chained controllers for port switching and a communications adapter for flow control. The urimeters have proven to be cost-effective, labor-saving devices. The PDMS interface provides a continuous display of measured variables and completely automates data entry for flowsheet documentation. Automatic urimetry data acquisition is estimated to save approximately 27 nursing minutes per patient per day.

New method for monitoring pulmonary artery catheter location.

OBJECTIVE To test the ability of a modified pulmonary artery (PA) monitoring catheter to detect distal catheter migration. DESIGN Prospective nonrandomized trial. PATIENTS Surgical ICU patients requiring invasive hemodynamic monitoring. INTERVENTIONS Eight patients received PA catheters modified to include a right ventricular (RV) pressure monitoring port located 7 cm from the tip. Fifteen patients received catheters with an RV port located 10 cm from the tip. Guided by the RV port pressure waveform, catheters were initially positioned so that the RV port was located just proximal to the pulmonic valve. MEASUREMENTS AND MAIN RESULTS Pulmonary capillary occlusion pressure (PAOP) could not be obtained in six of the eight patients receiving the 7-cm RV port catheter unless the RV port was advanced into the PA. PAOP was consistently obtained in all 15 patients receiving the 10-cm RV port catheter, with the RV port positioned in the RV. Chest radiographs confirmed a central PA catheter position. In this group, distal migration of the catheter occurred 14 times in eight patients, as detected by appearance of a PA pressure waveform at the RV port. Distal migration was corrected by withdrawal of the catheter until an RV waveform reappeared at the RV port. CONCLUSIONS We conclude that distal catheter migration occurs frequently with PA monitoring catheters, but can be detected at the bedside with a catheter modified to include an RV port 10 cm from the tip. This new catheter may add a margin of safety to PA monitoring and lower its overall cost by eliminating the need for chest radiographs ordered solely to confirm catheter tip location.

List-Mapped, List-Driven, Computerized Patient Care Records

Though several computerized documentation systems have been developed, not all of these methods offer the nurse a worthwhile alternative to hand written records. Those with multiple-choice type menus are usually inadequate in content and cumbersome to use.1 This type of charting may provide for increased accuracy of documentation, but there is no guarantee that the end result will be a relevant care plan or an effective record of the patient’s progress during a shift.2,3 Free-form text type notes are not systematically classifiable, and require the nurse to become a proficient typist in addition to his/her clinical duties.

Quality Assurance and Utilization Assessment: The Major By-Products of an Intensive Care Unit Clinical Information System

The power of computerized Clinical Information Systems (CIS) has yet to be tapped by most hospital Quality Assurance (QA) and Utilization Review (UR) departments. The CIS provides an economical and reliable means by which key clinical data can be extracted from the electronic chart and utilized for quality and utilization analyses. In comparison with current manual methods of extracting data by chart audits, the electronic method is not only faster, it also allows for every chart to be audited against standards for efficiency and quality of care. The science of industrial quality management is well known and appreciated in most other industries—many agree that the time is at hand for using these techniques in health care institutions [1]. The Joint Commission for Accredation of Health Care Organizations (JCAHO), the Health Care Financing Authority (HCFA), and other regulatory agencies now require detailed information and trends about outcomes that can not be easily obtained by traditional, tedious methods of manual chart review. However, this volume of data can objectively be extracted from the electronic record provided by a comprehensive CIS. A reduction in the number of hours spent by QA and UR nurses culling data from charts could be channeled into more meaningful activities of data interpretation and reporting. In this paper we describe the use of CIS-derived data for secondary QA and UR activities. ICUs that have a CIS are ready to enjoy the benefits such a system can provide for daily monitoring of patient care and resource activities.