Real time data acquisition: recommendations for the Medical Information Bus (MIB)

Patient length of stay and mortality prediction: A survey

Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.

Integrating Real Time Data to Improve Outcomes in Acute Kidney Injury

Critically ill patients with acute kidney injury requiring renal replacement therapy have a poor prognosis. Despite well-known factors, which contribute to outcomes, including dose delivery, patients frequently miss the target dose and volume removal. One major barrier to effective care of these patients is the traditional dissociation of dialysis device data from other clinical information systems, notably the electronic health record (EHR). This lack of integration and the resulting manual documentation leads to errors and biases in documentation and missed opportunities to intervene in a timely fashion. This review summarizes the technological advancements facilitating direct connection of dialysis devices to EHRs. This connection facilitates automated data capture of many variables - including delivered dose, ultrafiltration rate and pressure measurements - which in turn can be leveraged for data mining, quality improvement and real-time targeted therapy adjustments. These interventions hold the promise to significantly improve outcomes for this patient population.

Challenges Associated With Using Large Data Sets for Quality Assessment and Research in Clinical Settings

The rapidly expanding use of electronic records in health-care settings is generating unprecedented quantities of data available for clinical, epidemiological, and cost-effectiveness research. Several challenges are associated with using these data for clinical research, including issues surrounding access and information security, poor data quality, inconsistency of data within and across institutions, and a paucity of staff with expertise to manage and manipulate large clinical data sets. In this article, we describe our experience with assembling a data-mart and conducting clinical research using electronic data from four facilities within a single hospital network in New York City. We culled data from several electronic sources, including the institution's admission-discharge-transfer system, cost accounting system, electronic health record, clinical data warehouse, and departmental records. The final data-mart contained information for more than 760,000 discharges occurring from 2006 through 2012. Using categories identified by the National Institutes of Health Big Data to Knowledge initiative as a framework, we outlined challenges encountered during the development and use of a domain-specific data-mart and recommend approaches to overcome these challenges.

A cautionary tale of technology: not a substitute for careful collaboration and effective communication

This is an original case study highlighting the importance of careful collaboration and effective communication among healthcare providers. Amid the chaos of a new year, implementing a new healthcare act with an entirely new system of coding, patients are informed consumers interconnected through enhanced technological advances. This article describes a complicated situation involving long-term follow-up of a patient with a history of previous surgical removal of a rare tumor. The problem is one in which the patient is given exclusive access to a newly implemented electronic medical record system that has not fully completed the transition, including all healthcare specialties in the new system. What results is unnecessary, weeklong emotional turmoil, where the patient's health status is misinterpreted and miscommunicated. The challenge is helping patients become aggressive advocates and consumers of quality care, without breaching confidentiality. How much access is too much and what can happen if patients are given full disclosure and access to the electronic medical record? Fortunately, in this case, the problem is solved when the main provider returns from out-of-town, and multiple providers unite, carefully collaborate, and effectively communicate with the patient.

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.

Automated information systems in anesthesiology

The current state of the art of anesthesia information systems remains primitive. Currently, available commercial systems focus only at automating the charting process and not the care process. Until systems are available that integrate these two functions, anesthesiologists will not truly benefit from such systems.

A computerized data acquisition system for infusion devices--a clinical support tool, or a risk management tool?

A prototype computerized system for automatic data collection from multi-vendor infusion devices was constructed. The system was specifically designed around the needs of the critical care environment, and a survey of clinical staff was conducted to determine the functional requirements. Hardware, software and system configuration was based on the Medical Information Bus IEEE 1073 standard for medical device data communications. The infusion devices were configured into device communication controllers (DCC), which were polled at 0.25 Hz by a PC configured as a bedside communication controller (BCC). The system stores data samples after intervals of 1 ml of drug delivery and following any changes in the infusion rate. The system demonstrated significant opportunities for supporting clinical care and for the management of health care technology.

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.

Real time monitoring and analysis via the medical information bus, Part I

Because of the complexity of monitored data in modern intensive care units (ICUs), and the risk of information being overlooked if medical staff have to pay attention to a multiplicity of monitoring apparatuses and alarm signals, the data for each patient may well be best presented on a single bedside screen after digestion by expert system techniques. Such central units should be able to deal with data from any monitoring apparatus, not just a predefined set. Furthermore, relay of the information from each bed to a central control station (one per ICU) is desirable for the purposes of permanent storage and for in-depth analysis. The paper describes a comprehensive system for ICU monitoring management and patient data analysis that integrates multiple expert systems and computers. The basic difficulties in applying expert system techniques to monitoring are overcome with the shell OPS/83, which allows calls to sequential C routines and allows time-driven reasoning through appropriate design of the inference engine and rules. Flexibility as regards connectable monitoring apparatus is afforded by basing data acquisition mainly, though not exclusively, on the IEEE Medical Information Bus.

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.

Medical Information Bus usage for automated IV pump data acquisition: evaluation of usage patterns

OBJECTIVE

To identify factors which influence the choice of nurses to use automated collection of i.v. pump data from a prototype Medical Information Bus.

DESIGN

Observational study for a duration of three and one-half months.

SETTING

Four intensive care units, each with different missions, in an adult hospital.

SUBJECTS

One hundred fifty-eight registered nurses including both full and part time.

MEASUREMENTS AND MAIN RESULTS

Data were collected from the hospital information system about infusion orders including the type of medication, the number of rate changes, the method of documenting rate changes and the infusion methods. The method of documentation for infusion rate changes was defined as either automated, using a prototype Medical Information Bus (MIB), or manual, using the keyboard at a bedside computer terminal. The method of infusion was defined as either straight gravity feed without an i.v. pump ('no pump'), infusion using a pump but without connection to the hospital information system ('pump only') and infusion using a pump which was connected to the hospital information system using a prototype Medical Information Bus ('automated'). A total of 22,199 rate changes were documented during the study period and of those, 22,055 (99.35%) used the 'automated' method. Medications with the highest average rate change per single container were; Nitroprusside Sodium (9.50), Epinephrine (9.08) and Epoprostenol (7.50).

CONCLUSIONS

The nurses used automated i.v. pump data acquisition with medications which required frequent rate changes.

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.

Integrating computerized anesthesia charting into a hospital information system

BACKGROUND

Systems for computerization of anesthesia records have typically been 'stand-alone' computers many times connected to monitoring devices in the operating theater. A system was developed and tested at LDS Hospital in Salt Lake City, Utah, USA that was an integral part of the Health Evaluation through Logical Processing (HELP) hospital information system.

METHODS

The system was evaluated using time and motion studies to assess impact of the system on the anesthesiologists use of time, an assessment for completeness of the anesthesia record was conducted, and a questionnaire was used to assess anesthesiologists attitudes. Timing studies were performed on 44 surgical cases before computerization and 41 surgical cases after computerization. For both before and after computerization, about 80% of procedures were D&C, vaginal hysterectomy, laparoscopy, tubal ligation, or A&P repair.

RESULTS

The study showed a major reduction in time required for charting from 20.4% to 13.4% which was statistically significant (p = 0.0001). Other significant factors were a reduction in the time spent scanning the entire area which dropped from 10.5% to 5.6% (p = 0.001), patient preparation time increased from 10.1% to 13.1% (p = 0.02), the time spent arranging equipment increased from 6.4% to 8.1%, and the average time spent on non-anesthesia activities increased from 6.3% to 11.3%. The computerized anesthesia record was more legible, and complete than the manual record. The overall assessment of computer charting by anesthesiologists questionnaire was positive. The computerized anesthesia charting was preferred by the anesthesiologists, who, after one or two training sessions, used the system on their own.

CONCLUSIONS

It appears that having a computerized anesthesia charting system that is an integral part of a hospital information system not only saves anesthesiologists charting time, but also improves the quality of the record and was well accepted by busy private practice anesthesiologists.

Intelligent systems in patient monitoring and therapy management. A survey of research projects

Although today's advanced biomedical technology provides unsurpassed power in diagnosis, monitoring, and treatment, interpretation of vast streams of information generated by this technology often poses excessive demands on the cognitive skills of health-care personnel. In addition, storage, reduction, retrieval, processing, and presentation of information are significant challenges. These problems are most severe in critical care environments such as intensive care units (ICUs) and operating room (ORs) where many events are life-threatening and thus require immediate attention and the execution of definitive corrective actions. This article focuses on intelligent monitoring and control (IMC), or the use of artificial intelligence (AI) techniques to alleviate some of the common information management problems encountered in health-care environments. This article presents the findings of a survey of over 30 IMC projects. A major finding of the survey is that although significant advances have been made in introducing AI technology in critical care, successful examples of fielded systems are still few and far between. Widespread acceptance of these systems in critical care environments depends on a number of factors, including fruitful collaborations between clinicians and computer scientists, emphasis on evaluation studies, and easy access to clinical information.

Challenges and opportunities for computerizing the anesthesia record

Most observers would agree that the goal of computerizing the anesthesia record is a worthy one. Despite the fact that several academic groups and vendors have attempted to develop and provide computerized anesthesia charting, the practice is not widespread. In this review article, we attempt to outline the reasons for this reluctance to use computers for anesthesia charting. Where there are problems to be solved, there also are opportunities. We discuss the development of strategies to solve these problems and thus present opportunities for medical informatics professionals and anesthesiologists to work toward joint solutions. Solving these problems includes the development of consensus standards and working out technical, social, and educational difficulties. Details of the approaches recommended are outlined.

Evaluation of user acceptance of a clinical expert system

OBJECTIVE

To measure the attitudes of physicians and nurses who use the Health Evaluation through Logical Processing (HELP) clinical information system.

DESIGN

Questionnaire survey of 360 attending physicians and 960 staff nurses practicing at the LDS Hospital. The physicians' responses were signed, permitting follow-up for nonresponse and use of demographic data from staff files. The nurses' responses were anonymous and their demographic data were obtained from the questionnaires.

MEASUREMENTS

Fixed-choice questions with a Likert-type scale, supplemented by free-text comments. Question categories included: computer experience; general attitudes about impact of the system on practice; ranking of available functions; and desired future capabilities.

RESULTS

The response rate was 68% for the physicians and 39% for the nurses. Age, specialty, and general computer experience did not correlate with attitudes. Access to patient data and clinical alerts were rated highly. Respondents did not feel that expert computer systems would lead to external monitoring, or that these systems might compromise patient privacy. The physicians and nurses did not feel that computerized decision support decreased their decision-making power.

CONCLUSION

The responses to the questionnaire and "free-text comments" provided encouragement for future development and deployment of medical expert systems at LDS Hospital and sister hospitals. Although there has been some fear on the part of medical expert system developers that physicians would not adapt to or appreciate recommendations given by these systems, the results presented here are promising and may be of help to other system developers and evaluators.