Computerized management of patient care in a complex, controlled clinical trial in the intensive care unit

Artificial Intelligence in the Intensive Care Unit

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Computerised Decision Support for Women’s Health Informatics

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

Clinical decision support capabilities of commercially-available clinical information systems

BACKGROUND

The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems.

METHODS

The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features.

RESULTS

Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern.

CONCLUSIONS

These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

A four-phase model of the evolution of clinical decision support architectures

BACKGROUND

A large body of evidence over many years suggests that clinical decision support systems can be helpful in improving both clinical outcomes and adherence to evidence-based guidelines. However, to this day, clinical decision support systems are not widely used outside of a small number of sites. One reason why decision support systems are not widely used is the relative difficulty of integrating such systems into clinical workflows and computer systems.

PURPOSE

To review and synthesize the history of clinical decision support systems, and to propose a model of various architectures for integrating clinical decision support systems with clinical systems.

METHODS

The authors conducted an extensive review of the clinical decision support literature since 1959, sequenced the systems and developed a model.

RESULTS

The model developed consists of four phases: standalone decision support systems, decision support integrated into clinical systems, standards for sharing clinical decision support content and service models for decision support. These four phases have not heretofore been identified, but they track remarkably well with the chronological history of clinical decision support, and show evolving and increasingly sophisticated attempts to ease integrating decision support systems into clinical workflows and other clinical systems.

CONCLUSIONS

Each of the four evolutionary approaches to decision support architecture has unique advantages and disadvantages. A key lesson was that there were common limitations that almost all the approaches faced, and no single approach has been able to entirely surmount: (1) fixed knowledge representation systems inherently circumscribe the type of knowledge that can be represented in them, (2) there are serious terminological issues, (3) patient data may be spread across several sources with no single source having a complete view of the patient, and (4) major difficulties exist in transferring successful interventions from one site to another.

Assessing data quality in manual entry of ventilator settings

OBJECTIVE

To evaluate the data quality of ventilator settings recorded by respiratory therapists using a computer charting application and assess the impact of incorrect data on computerized ventilator management protocols. DESIGN An analysis of 29,054 charting events gathered over 12 months from 678 ventilated patients (1,736 ventilator days) in four intensive care units at a tertiary care hospital.

MEASUREMENTS

Ten ventilator settings were examined, including fraction of inspired oxygen (Fio (2)), positive end-expiratory pressure (PEEP), tidal volume, respiratory rate, peak inspiratory flow, and pressure support. Respiratory therapists entered values for each setting approximately every two hours using a computer charting application. Manually entered values were compared with data acquired automatically from ventilators using an implementation of the ISO/IEEE 11073 Medical Information Bus (MIB). Data quality was assessed by measuring the percentage of time that the two sources matched. Charting delay, defined as the interval between data observation and data entry, also was measured.

RESULTS

The percentage of time that settings matched ranged from 99.0% (PEEP) to 75.9% (low tidal volume alarm setting). The average charting delay for each charting event was 6.1 minutes, including an average of 1.8 minutes spent entering data in the charting application. In 559 (3.9%) of 14,263 suggestions generated by computerized ventilator management protocols, one or more manually charted setting values did not match the MIB data.

CONCLUSION

Even at institutions where manual charting of ventilator settings is performed well, automatic data collection can eliminate delays, improve charting efficiency, and reduce errors caused by incorrect data.

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.

Decision support system to assist mechanical ventilation in the adult respiratory distress syndrome

This paper presents a knowledge-based decision support system to assist mechanical ventilation in patients with the Adult Respiratory Distress Syndrome (DSSARDS). The knowledge base uses clinical algorithms developed from interviews and seminars with experts. The system contains 140 rules, applies backward chaining and was built on an IBM-PC compatible microcomputer. Clinical and physiological data and ventilator settings were used for suggestions of ventilatory support mode (VSMODE) and settings (MVSET) and for hemodynamic evaluation and therapy (HEMO). Success rates (s) and kappa coefficient (k) were used to measure agreement between DSSARDS and physicians at 4 decision steps related to: beginning of mechanical ventilation (FIRSTSET), VSMODE, MVSET and HEMO, DSSARDS prototype was evaluated in a development phase with 6 patients aged 48.6 +/- 15.9 years. Agreement results for 142 decision steps were: FIRSTSET k = 0.90, s = 0.93; VSMODE k = 0.76, s = 0.92; HEMO k = 0.58, s = 0.70, MVSET k = 0.86, s = 0.92 (p < 0.05 for all k). Improvements in the knowledge base were performed mainly in HEMO and VSMODE modules. The subsequent test phase studied 5 patients aged 54.8 +/- 11.0 years in a total of 900 decision steps. Results were: FIRSTSET k = 0.93, s = 0.95; VSMODE k = 0.93, s = 0.96; HEMO k = 0.97, s = 0.99, MVSET k = 0.96, s = 0.97 (p < 0.05 for all k). The results indicate significant agreement between DSSARDS and physicians for all decision steps. This suggests that DSSARDS may be used as a support for decision making and a training tool for mechanical ventilation in patients with the adult respiratory distress syndrome.

The role of medical informatics in telemedicine

The role of medical informatics in telemedicine is dependent on using the power of the computerized database to not only feed patient specific information to the health care providers, but to use the epidemiological and statistical information in the data base to improve decision making and ultimately care. The computer is also a powerful tool to facilitate standardizing and monitoring of care and when applied in continuous quality improvement methodology it can enhance the improvement process well beyond what can be done by hand. The coupling of medical informatics with telemedicine allows sophisticated medical informatics systems to be applied in low population density and remote areas.

A clinical decision support system prototype for cardiovascular intensive care

This paper describes the development and validation of a decision-support system prototype that can help manage hypovolemic hypotension in the Cardiovascular Intensive Care Unit (CVICU). The prototype uses physiologic pattern-matching, therapeutic protocols, computational drug-dosage response modeling and expert reasoning heuristics in its selection of intervention strategies and choices. As part of model testing, the prototype simulated real-time operation by processing historical physiologic and intervention data on a patient sequentially, generating alerts on questionable data, critiques of interventions instituted and recommendations on preferred interventions. Bench-testing with 399 interventions from 13 historical cases showed therapies for bleeding and fluid replacement proposed by the prototype were significantly more consistent (p < 0.0001) than those instituted by the staff when compared against expert critiques (80% versus 44%). This study has demonstrated the feasibility of formalizing hemodynamic management of CVICU patients in a manner that may be implemented and evaluated in a clinical setting.

Designing an outcome-oriented computer decision-support system for cardiovascular ICU--a preliminary report

This paper describes the conceptual framework and preliminary results of an outcome-oriented decision-support system prototype for the cardiovascular intensive care unit (CVICU). The major characteristics of this design include: (1) its problem-based approach to solving clinical problems; (2) an integrated structure with the hospital information system in terms of its data, model and knowledge bases; (3) proposed alternative modes of interaction that include monitoring and critiquing; (4) and research modules that design, manage, and analyze outcome-based clinical studies. At present, an initial prototype has been implemented on a PC as a set of modules accessible from a main menu. The structural framework of the overall system is fairly well defined but only limited quantitative, statistical and expert knowledge has been captured. The second phase of the project involves porting the prototype to a Unix workstation environment, refining and adding models to the model base, expanding its knowledge bases, reasoning capability, and testing the prototype with actual clinical cases in a real-time fashion.

Medical informatics in the intensive care unit: state of the art 1991

Intensive care medicine requires timely, accurate, and integrated patient records to provide the highest quality patient care. Computerized patient records offer the best method to achieve these needs. The expectations of society for medical progress through increased use of computers is growing. For optimal use of computers in the ICU there must be a harmonious collaboration between medical informaticists, physicians, nurses, therapists, and administrators. The future use of computers in ICU care will be evolutionary rather than revolutionary. We are on the frontier of some exciting times in the next decade as computers become commonplace in the clinical care process rather than an unusual event. This paper discusses the progress and challenges of computers in the ICU.

INFORM: integrated support for decisions and activities in intensive care

Many medical decision support systems that have been developed in the past have failed to enter routine clinical practice. Often this is because the developers have failed to analyse in sufficient detail the precise user requirements, because they have produced a system which takes too narrow a view of the patient, or because the decision support facilities have not been sufficiently well integrated into the routine clinical data handling activities. In this paper we discuss how the AIM-INFORM project is setting out to deal with these issues, in the context of the provision of decision support in the intensive care unit.

Increased survival of ARDS patients with severe hypoxemia (ECMO criteria)

The adult respiratory distress syndrome (ARDS) is a form of diffuse lung injury associated with multiple risk factors. Patients with severe hypoxemia who meet blood gas criteria defined by the extracorporeal membrane oxygenation trial (ECMO) of 1974 to 1977 have a reported survival of 11 percent. The reported survival has remained unchanged for 15 years despite numerous technologic advances. We prospectively studied ARDS patients who met ECMO blood gas criteria. One hundred seventy-eight ARDS patients were prospectively screened over a 30-month period. Fifty-one of these patients met ECMO blood gas criteria and 23 (45 percent) survived (p less than 0.001 vs ECMO trial). No obvious differences in etiology, APACHE II score, organ system failure, or the incidence of sepsis was found between survivors and nonsurvivors. We conclude that survival of ARDS patients who met ECMO blood gas criteria in our institution is higher than that previously reported from both other centers and our own hospital.

The value of an expert system in performing clinical drug trials

An expert system has been designed to assist the cardiologist in determining whether patients can be included in clinical trials. This system contains knowledge on inclusion and exclusion criteria for six drug trials, and has been validated in 100 randomly selected patients. In 97 cases, the expert system and the cardiologist made an identical classification; in the remaining three cases, the patient was incorrectly classified by the physician. The system will also optimize the order in which questions are asked in order to minimize the time required to decide on inclusion or exclusion.

Computers in critical care: opportunities and challenges

Treating acutely ill patients in intensive care units (ICUs) requires assimilating large amounts of patient data. The computer can help process these data and display information in easy to understand formats. Also, knowledge-based systems can provide advice in diagnosis and treatment of common disorders in the ICU. For effective use of computers, systems must be integrated into the total hospital information system and computer data must logically become the primary medical record. Standards are being developed to aid in this process. Although computers have been used in the ICU for 25 years, most hospitals still use the paper medical record. Prototype systems such as the HELP, CARE, and PDMS systems are described. They are integrated ICU systems for computerizing most of the traditional functions in the ICU. Several commercial information management products are also described along with recently developed computerized drug and fluid delivery systems. Finally, prototype knowledge-based programs are presented that provide advice to the clinician on such topics as acid-base balance, hemodynamic monitoring, and shock management.

Clinical evaluation of computer-based respiratory care algorithms

A collection of computer-based respiratory care algorithms were implemented as a prototype computer-based patient advice system (COMPAS) within the existing HELP hospital information system. Detailed medical logic recommended ventilator adjustments for 5 different modes of ventilation: assist/control (A/C), intermittent mandatory ventilation (IMV), continuous positive airway pressure (CPAP), pressure controlled inverted ratio ventilation (PC-IRV), and extracorporeal carbon dioxide removal (ECCO2R). Suggestions for adjusting the mode of ventilation, fraction of inspired oxygen (FiO2), positive end-expiratory pressure (PEEP), peak inspiratory pressure, and several other therapeutic measures related to the treatment of severe arterial hypoxemia in adult respiratory distress syndrome (ARDS) patients were automatically presented to the clinical staff via bedside computer terminals. COMPAS was clinically evaluated for 624 hours of patient care on the first 5 ARDS patients in a randomized clinical trial. The clinical staff carried out 84% (320/379) of the computerized therapy suggestions. In response to a questionnaire distributed to clinical users of the system, 86% judged the system to be potentially valuable. Through implementation of COMPAS, a computer-based ventilatory therapy advice system, we have laid the groundwork for standardization of ventilator management of arterial hypoxemia in critically ill ARDS patients.