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Biomedical subjects

T D East

Publications and source records attributed to T D East.

At least 19 recordsLinked to original sources
PubMed2001

Computerized decision support for mechanical ventilation of trauma induced ARDS: results of a randomized clinical trial.

BACKGROUND: Variability and logistic complexity of mechanical ventilatory support of acute respiratory distress syndrome, and need to standardize care among all clinicians and patients, led University of Utah/LDS Hospital physicians, nurses, and engineers to develop a comprehensive computerized protocol. This bedside decision support system was the basis of a multicenter clinical trial (1993-1998) that showed ability to export a computerized protocol to other sites and improved efficacy with computer- versus physician-directed ventilatory support. The Memorial Hermann Hospital Shock Trauma intensive care unit (ICU) (Houston, TX; a Level I trauma center and teaching affiliate of The University of Texas Houston Medical School) served as one of the 10 trial sites and recruited two thirds of the trauma patients. Results from the trauma patient subgroup at this site are reported to answer three questions: Can a computerized protocol be successfully exported to a trauma ICU? Was ventilator management different between study groups? Was patient outcome affected? METHODS: Sixty-seven trauma patients were randomized at the Memorial Hermann Shock Trauma ICU site. "Protocol" assigned patients had ventilatory support directed by the bedside respiratory therapist using the computerized protocol. "Nonprotocol" patients were managed by physician orders. RESULTS: Of the 67 trauma patients randomized, 33 were protocol (age 40 +/- 3; Injury Severity Score [ISS] 26 +/- 3; 73% blunt) and 34 were nonprotocol (age 38 +/- 2; ISS 25 +/- 2; 76% blunt). For the protocol group, the computerized protocol was used 96% of the time of ventilatory support and 95% of computer-generated instructions were followed by the bedside respiratory therapist. Outcome measures (i.e., survival, ICU length of stay, morbidity, and barotrauma) were not significantly different between groups. Fio2 > or = 0.6 and Pplateau > or = 35 cm H2O exposures were less for the protocol group. CONCLUSION: A computerized protocol for bedside decision support was successfully exported to a trauma center, and effectively standardized mechanical ventilatory support of trauma-induced acute respiratory distress syndrome without adverse effect on patient outcome.

Adult↗
PubMed1999

Efficacy of computerized decision support for mechanical ventilation: results of a prospective multi-center randomized trial.

200 adult respiratory distress syndrome patients were included in a prospective multicenter randomized trial to determine the efficacy of computerized decision support. The study was done in 10 medical centers across the United States. There was no significant difference in survival between the two treatment groups (mean 2 = 0.49 p = 0.49) or in ICU length of stay between the two treatment groups when controlling for survival (F(1df) = 0.88, p = 0.37.) There was a significant reduction in morbidity as measured by multi-organ dysfunction score in the protocol group (F(1df) = 4.1, p = 0.04) as well as significantly lower incidence and severity of overdistension lung injury (F(1df) = 45.2, p < 0.001). We rejected the null hypothesis. Efficacy was best for the protocol group. Protocols were used for 32,055 hours (15 staff person years, 3.7 patient years or 1335 patient days). Protocols were active 96% of the time. 38,546 instructions were generated. 94% were followed. This study indicates that care using a computerized decision support system for ventilator management can be effectively transferred to many different clinical settings and significantly improve patient morbidity.

Adult↗
PubMed1999

Clinical informatics: 2000 and beyond.

Healthcare has begun to flounder in the mounting flood of data available from automated monitoring equipment, microprocessor controlled life-support equipment, such as ventilators, ever more sophisticated laboratory tests, and the myriad of minor technological wonders that every hospital and clinic seem to collect. It is no longer enough to merely display the data in a large spreadsheet or on a complex, colorful time-sequence graph. The next generation of healthcare information systems must help the clinician to assimilate the myriad of data and to make fast and effective decisions. The following is a list of features that the next generation of computer systems will have to include if they are to have a significant impact on the quality of patient care: data acquisition, data storage, information display, data processing, and decision support. By automating or streamlining repetitive or complex tasks, correlating and presenting complex and potentially confusing data, and tracking patient outcomes, the computer can augment clinicians' skills to improve patient care.

Computer Systems↗
PubMed1999

Effectiveness of an information broker service.

The many disparate databases existing within the same health care organization create confusion and frustration for the consumer trying to get integrated information. The purpose of this research was to create a regional service that would provide a customer oriented information broker service with a single point of contact and guaranteed performance. From 1/98-6/99 there were 34 requests made. 23 were completed on time, eight still in progress and three were late (86.46% on time, average late time 0.76 days). 13 clinical departments used the service. Data was integrated from twelve different data sources. The requests produced about 2 Gigabytes of integrated data (416,666 single spaced pages). The resources required were approximately 1.3 FTE ($65K in direct costs). The cost/page of integrated information was 19 cents. The benefit to cost ratio was at least 3 and most likely higher. Surveys of customers indicated high satisfaction with services and would both utilize the service again and recommend it to others.

Commerce↗
PubMed1998

Evaluation of compliance with a computerized protocol: weaning from mechanical ventilator support using pressure support.

STUDY OBJECTIVES: To use a computerized consultation system to evaluate the feasibility of a mechanical ventilator weaning protocol which used the rapid shallow breathing index to guide adjustments in pressure support. A program to monitor user compliance and reasons for noncompliance was built into the computerized consultation system. METHODS: A total of nine critically ill patients (ten weaning episodes) were enrolled in the protocol. The respiratory therapists performed routine computer charting in the electronic database. They accepted or declined the explicit instructions generated by the computerized protocol and displayed on the bedside terminal. The consultation program monitored whether accepted instructions were implemented by the user. RESULTS: Patient's therapy was controlled by protocol for a total of 1075 h (mean 108 h, range 4 to 339 h) and 94.8% (1321/1394) of instructions were followed by the clinical staff. Of the 42 instructions clinical staff refused to follow, 23 (55%) were extubation instructions. There were 52 (3.7%) incorrect instructions generated with 24 software errors, 21 errors in underlying logic, and seven user misunderstanding errors. CONCLUSIONS: A high level of user compliance with this protocol was achieved. The methods described herein to monitor compliance and reasons for noncompliance within a protocol are reusable in the domain of mechanical ventilation and possibly in other domains.

Evaluation Studies as Topic↗
PubMed1998

Automated key process monitors for patient care documentation.

Complete, accurate and timely patient care documentation is an essential part of the practice of medicine. As with any other process in medicine or industry, continuous quality improvement (CQI) is essential to assure the highest quality at the lowest cost. CQI requires objective key process measures that can be assessed routinely. A set of key process monitors designed to assess completeness, accuracy and timeliness were created based on local, regional and national standards. Feasibility was assessed in the LDS Hospital Emergency Department using 31,429 patient visits in the 18 months from June 1995 to November 1996. The logic of the score was programmed into SQL scripts and run against an Oracle database containing the patient care documentation. The results indicate that the chosen key process monitors can be used to provide real time assessment of the patient care documentation process. The general concepts of the key process measures of completeness, accuracy and timeliness are generalizable to many areas of medicine. The overall score provides one method of easily tracking departmental performance while the overall process monitoring database allows powerful, in-depth analysis of individual components of the process. It is recommended that such automated process monitoring tools be integrated into future clinical information systems.

Data Interpretation, Statistical↗
PubMed1997

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.

Adult↗
PubMed1996

Current status of mechanical ventilation decision support systems: a review.

Objectives of computerized decision support systems for mechanical ventilation are discussed. Questions considered are: Why is computerized decision support for mechanical ventilation important? What parameter(s) should be optimized? What are the differences between a single attribute and a multiattribute value function used for optimization? How is it possible to achieve optimization in clinical practice with existing ventilators? How does one solve the problem of acquiring measurement of data needed for closed loop control? The possibilities and limitations of three existing decision support systems are discussed. 1) Computerized protocols from LDS Hospital in Salt Lake City, Utah, USA. 2) Optimization Program (OPTPROG) developed jointly at the Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland and Medical Intensive Care Unit, Department of Medicine at Karolinska Institute, South Hospital, Stockholm, Department of Medical Informatics Linkoping University, Sweden. 3) Ventilator Therapy Planner (VENT-PLAN) from the Section on Medical Informatics at Stanford University, Palo Alto, California, USA. Strategies leading to an optimal computerized decision support system are proposed. These strategies include development of better measurement methods for blood gases and cardiac output, improvement of man-machine and machine-machine interaction and the selection of optimization criteria. Finally, research directed towards building quantitative, dynamic patient models based on computerized databases of mechanically ventilated patients are discussed.

Algorithms↗
PubMed1996

Testing and validation of computerized decision support systems.

Systematic, through testing of decision support systems (DSSs) prior to release to general users is a critical aspect of high quality software design. Omission of this step may lead to the dangerous, and potentially fatal, condition of relying on a system with outputs of uncertain quality. Thorough testing requires a great deal of effort and is a difficult job because tools necessary to facilitate testing are not well developed. Testing is a job ill-suited to humans because it requires tireless attention to a large number of details. For these reasons, the majority of DSSs available are probably not well tested prior to release. We have successfully implemented a software design and testing plan which has helped us meet our goal of continuously improving the quality of our DSS software prior to release. While requiring large amounts of effort, we feel that the process of documenting and standardizing our testing methods are important steps toward meeting recognized national and international quality standards. Our testing methodology includes both functional and structural testing and requires input from all levels of development. Our system does not focus solely on meeting design requirements but also addresses the robustness of the system and the completeness of testing.

Artificial Intelligence↗
PubMed1996

Standardization of clinical decision making for the conduct of credible clinical research in complicated medical environments.

The likelihood that past experience will produce correct guides to current practice depends on the signal-to-noise ratio for the clinical problem of interest. If the signal-to-noise ratio is high, the decision will be sound and patient benefit likely to occur. If the signal-to-noise ratio is low, as is commonly the case with difficult clinical decisions, then personal experience and the best intentions will not assure sound clinical decisions. When the probability of benefit cannot be quantified, clinicians in complex settings are in danger of being misled by data and experience. Quantifiable probabilities established by group experiment or observation will be necessary for clinical decisions that can be expected to confer benefit on the patient. Explicit methods are necessary for interventions that can be replicated in experiments or in practice. Computerized protocols force the articulation of explicit clinical care methods and standardize clinical decision making. We have developed explicit, rule-based protocols, implemented them in our hospital, exported them to other hospitals, and successfully achieved a rigorous experimental environment in the clinical ICU. Exportation of such explicit methods may narrow the gap between efficacy (university hospital) and effectiveness (community hospital) research results.

Clinical Trials as Topic↗
PubMed1995

Computers in critical care.

This article reviews the current state-of-the-art and future applications of computers in critical care, with particular attention to ventilator and drug-delivery applications. Automated charting, alerts and alarms, and tools for decision support (such as expert systems and closed-loop control) are discussed also.

Computers↗
PubMed1995

Verification & validation algorithms for data used in critical care decision support systems.

A decision support system is only as good as the data generating that decision support system. If the data is incorrect, doesn't relate to the other pieces of data, is missing or is not consistent, the decision support system conclusions may be incorrect and inconsistent. While collecting data from several sites during a multicenter randomized clinical trial, we found that some critical data elements were missing, out of correct ranges, totally illogical, and/or inconsistently recorded. In order to get consistent, correct, and dependable information from a our decision support system, the data elements used in that system had to be checked for completeness, valid values, consistent units of measurement, and relationships to other items. Development of data quality assurance rules and the application of those rules is imperative to using the data to generate daily scores for multiple organ failure, sepsis, and barotrauma.

Algorithms↗
PubMed1995

Medical informatics academia and industry: a symbiotic relationship that may assure survival of both through health care reform.

There are often clear lines drawn identifying the demilitarized zone between medical informatics academics and industry. Academics were "pure" intellectuals sequestered in ivory towers that effectively shielded them from the realities of the world. Industry has historically focused on creating effective products that produce financial return to the corporation. Both the paradigms of academia and industry are quickly becoming dinosaurs in the era of health care reform where both medical informatics academia and industry are under increasing pressure to develop and prove that medical informatics has a positive impact on health care both in terms of the quality of care as well as cost. Unfortunately, neither academia or industry alone are going to be able to successfully complete this task. The purpose of this paper is to describe such a collaborative effort that has produced a computerized decision support system for the management of mechanical ventilation in patients with the Adult Respiratory Distress Syndrome (ARDS) that is now installed and supported on three different commercial CIS platforms. This collaborative effort has allowed us to successfully mount a large multi-center clinical trial designed to determine efficacy.

Clinical Protocols↗
PubMed1995

A successful protocol for the use of pulse oximetry to classify arterial oxygenation into four fuzzy categories.

Pulse oximetry is widely used in critical care medicine to noninvasively estimate arterial hemoglobin oxygen saturation. Despite the obvious benefits of using pulse oximetry to detect life threatening desaturations, it is unknown how well pulse oximetry is able to predict the finer graduations of arterial oxygenation needed for clinical decision making. A computerized protocol was developed for the use of pulse oximetry to classify arterial oxygenation into four fuzzy categories and tested in a prospective clinical trial which compared the oxygenation category assigned by the protocol to one assigned by a respiratory therapist. In 3,742 classifications from 15 patients over a seven month period, the protocol showed 96% agreement with the therapists in the direction of therapy and 75% agreement with the oxygenation classes assigned by the therapists.

Clinical Protocols↗
PubMed1994

Decision support in medicine: examples from the HELP system.

Computerized health information systems can contribute to the care received by patients in a number of ways. Not the least of these is through interactions with health care providers to modify diagnostic and therapeutic decisions. Since its beginning, developers have used the HELP hospital information system to explore computerized interventions into the medical decision making process. By their nature these interventions imply a computer-directed interaction with the physicians, nurses, and therapists involved in delivering care. In this paper we describe four different approaches to this intervention. These include: (1) processes that respond to the appearance of certain types of clinical data by issuing an alert informing caregivers of these data's presence and import, (2) programs that critique new orders and propose changes in those orders when appropriate, (3) programs that suggest new orders and procedures in response to patient data suggesting their need, and (4) applications that function by summarizing patient care data and that attempt to retrospectively assess the average or typical quality of medical decisions and therapeutic interventions made by health care providers. These approaches are illustrated with experience from the HELP system.

Adult↗
PubMed1994

Randomized clinical trial of pressure-controlled inverse ratio ventilation and extracorporeal CO2 removal for adult respiratory distress syndrome.

The impact of a new therapy that includes pressure-controlled inverse ratio ventilation followed by extracorporeal CO2 removal on the survival of patients with severe ARDS was evaluated in a randomized controlled clinical trial. Computerized protocols generated around-the-clock instructions for management of arterial oxygenation to assure equivalent intensity of care for patients randomized to the new therapy limb and those randomized to the control, mechanical ventilation limb. We randomized 40 patients with severe ARDS who met the ECMO entry criteria. The main outcome measure was survival at 30 days after randomization. Survival was not significantly different in the 19 mechanical ventilation (42%) and 21 new therapy (extracorporeal) (33%) patients (p = 0.8). All deaths occurred within 30 days of randomization. Overall patient survival was 38% (15 of 40) and was about four times that expected from historical data (p = 0.0002). Extracorporeal treatment group survival was not significantly different from other published survival rates after extracorporeal CO2 removal. Mechanical ventilation patient group survival was significantly higher than the 12% derived from published data (p = 0.0001). Protocols controlled care 86% of the time. Average PaO2 was 59 mm Hg in both treatment groups. Intensity of care required to maintain arterial oxygenation was similar in both groups (2.6 and 2.6 PEEP changes/day; 4.3 and 5.0 FIO2 changes/day). We conclude that there was no significant difference in survival between the mechanical ventilation and the extracorporeal CO2 removal groups. We do not recommend extracorporeal support as a therapy for ARDS. Extracorporeal support for ARDS should be restricted to controlled clinical trials.

Adult↗
PubMed1994

A model-based simulator for testing rule-based decision support systems for mechanical ventilation of ARDS patients.

A model-based simulator was developed for testing rule-based decision support systems that manages ventilator therapy of patients with the Adult Respiratory Distress Syndrome (ARDS). The simulator is based on a multi-compartment model of the human body and mathematical models of the gas exchange abnormalities associated with ARDS. Initial testing of this system indicates that model-based simulators are a viable tool for testing rule-based expert systems used in health-care.

Computer Simulation↗