Monthly Archives: November 2013

Analog issues in digital circuits

Digital circuits are made from analog components. The design must assure that the analog nature of the components doesn’t dominate the desired digital behavior. Digital systems must manage noise and timing margins, parasitic inductances and capacitances, and filter power connections.

Bad designs have intermittent problems such as "glitches", vanishingly-fast pulses that may trigger some logic but not others, "runt pulses" that do not reach valid "threshold" voltages, or unexpected ("undecoded") combinations of logic states.

Since digital circuits are made from analog components, digital circuits calculate more slowly than low-precision analog circuits that use a similar amount of space and power. However, the digital circuit will calculate more repeatably, because of its high noise immunity. On the other hand, in the high-precision domain (for example, where 14 or more bits of precision are needed), analog circuits require much more power and area than digital equivalents.

Hospital Information Systems: Their Function and State

175.1

Patient Database Strategies for the HIS

175.2

Data Acquisition

175.3

Patient Admission, Transfer, and Discharge Functions

175.4

Patient Evaluation

T. Allan Pryor

175.5

Patient Management

University of Utah

175.6

Conclusion

The definition of a hospital information system (HIS) is unfortunately not unique. The literature of both the informatics community and health care data processing world is filled with descriptions of many differing computer systems defined as an HIS. In this literature, the systems are sometimes characterized into varying level of HISs according to the functionally present within the system. With this confusion from the literature, it is necessary to begin this chapter with a definition of an HIS. To begin this definition,

Must first describe what it is not. The HIS will incorporate information from the several departments within the hospital, but an HIS is not a departmental system. Departmental systems such as a pharmacy or a radiology system are limited in their scope. They are designed to manage only the department that they serve and rarely contain patient data captured from other departments. Their function should be to interface with the HIS and provide portions of the patient medical/administrative record that the HIS uses to manage the global needs of the hospital and patient.

A clinical information system is likewise not an HIS. Again, although the HIS needs clinical information to meets its complete functionality, it is not exclusively restricted to the clinical information supported by the clinical information systems. Examples of clinical information systems are ICU systems, respiratory care systems, nursing systems. Similar to the departmental systems, these clinical systems tend to be one­dimensional with a total focus on one aspect of the clinical needs of the patient. They provide little support for the administrative requirements of the hospital.

If we look at the functional capabilities of both the clinical and departmental systems, we see many common features of the HIS. They all require a database for recording patient information. Both types of systems must be able to support data acquisition and reporting of patient data. Communication of information to other clinical or administrative departments is required. Some form of management support can be found in all the systems. Thus, again looking at the basic functions of the system one cannot differentiate the clinical/departmental systems from the HIS. It is this confusion that makes defining the HIS difficult and explains why the literature is ambiguous in this matter.

The concept of the HIS appears to be, therefore, one of integration and breadth across the patient or hospital information needs. That is, to be called an HIS the system must meet the global needs of those it is to serve. In the context, if we look at the hospital as the customer of the HIS, then the HIS must be able to provide global and departmental information on the state of the hospital. For example, if we consider the capturing of charges within the hospital to be an HIS function, then the system must capture all patient charges no matter which departmental originated those charges. Likewise all clinical informa­tion about the patient must reside within the database of the HIS and make possible the reporting and management of patient data across all clinical departments and data sources. It is totality of function that differentiates the HIS from the departmental or restricted clinical system, not the functions provided to a department or clinical support incorporated within the system.

The development of an HIS can take many architectural forms. It can be accomplished through interfacing of a central system to multiple departmental or clinical information systems. A second approach which has been developed is to have, in addition to a set of global applications, departmental or clinical system applications. Because of the limitation of all existing systems, any existing comprehen­sive HIS will in fact be a combination of interfaces to departmental/clinical systems and the applica­tions/database of the HIS purchased by the hospital.

The remainder of this chapter will describe key features that must be included in today’s HIS. The features discussed below are patient databases, patient data acquisition, patient admission/bed control, patient management and evaluation applications, and computer-assisted decision support. This chapter will not discuss the financial/administrative applications of an HIS, since those applications for the purposes of this chapter are seen as applications existing on a financial system that may not be integral application of the HIS.

Patient Database Strategies for the HIS

The first HISs were considered only an extension of the financial and administrative systems in place in the hospital. With this simplistic view many early systems developed database strategies that were limited in their growth potential. Their databases mimicked closely the design of the financial systems that presented a structure that was basically a “flat file” with well-defined fields. Although those fields were adequate for capturing the financial information used by administration to track the patient’s charges, they were unable to adapt easily to the requirement to capture the clinical information being requested by health care providers. Today’s HIS database should be designed to support a longitudinal patient record (the entire clinical record of the patient spanning multiple inpatient, outpatient encounters), integration of all clinical and financial data, and support of decision support functions.

The creation of a longitudinal patient record is now a requirement of the HIS. Traditionally the databases of the HISs were encounter-based. That is, they were designed to manage a single patient visit to the hospital to create a financial record of the visit and make available to the care provider data recorded during the visit. Unfortunately, with those systems the care providers were unable to view the progress of the patient across encounters, even to the point that in some HISs critical information such as patient allergies needed to be entered with each new encounter. From the clinical perspective, the management of a patient must at least be considered in the context of a single episode of care. This episode might include one or more visits to the hospital’s outpatient clinics, the emergency department, and multiple inpatient stays. The care provider to manage properly the patient, must have access to all the information recorded from those multiple encounters. The need for a longitudinal view dictates that the HIS database structure must both allow for access to the patient’s data independent of an encounter and still provide for encounter-based access to adapt to the financial and billing requirements of the hospital.

The need for integration of the patient data is as important as the longitudinal requirement. Tradi­tionally the clinical information tended to be stored in separate departmental files. With this structure it was easy to report from each department, but the creation of reports combining data from the different proved difficult if not impossible. In particular in those systems where access to the departmental data was provided only though interfaces with no central database, it was impossible to create an integrated patient evaluation report. Using those systems the care providers would view data from different screens at their terminal and extract with pencil onto paper the results from each departmental (clinical labo­ratory, radiology, pharmacy, and so on) the information they needed to properly evaluate the patient. With the integrated clinical database the care provider can view directly on a single screen the information from all departments formatted in ways that facilitate the evaluation of the patient.

Today’s HIS is no longer merely a database and communication system but is an assistant in the management of the patient. That is, clinical knowledge bases are an integral part of the HIS. These knowledge bases contain rules and/or statistics with which the system can provide alerts or reminders or implement clinical protocols. The execution of the knowledge is highly dependent on the structure of the clinical database. For example, a rule might be present in the knowledge base to evaluate the use of narcotics by the patient. Depending on the structure of the database, this may require a complex set of rules looking at every possible narcotic available in the hospital’s formulary or a single rule that checks the presence of the class narcotics in the patient’s medical record. If the search requires multiple rules, it is probably because the medical vocabulary has been coded without any structure. With this lack of structure there needs to be a specific rule to evaluate every possible narcotic code in the hospital’s formulary against the patient’s computer medication record. With a more structured data model a single rule could suffice. With this model the drug codes have been assigned to include a hierarchical structure where all narcotics would fall into the same hierarchical class. Thus, a single rule specific only to the class “narcotics” is all that is needed to compare against the patient’s record.

These enhanced features of the HIS database are necessary if the HIS is going to serve the needs of today’s modern hospital. Beyond these inpatient needs, the database of the HIS will become part of an enterprise clinical database that will include not only the clinical information for the inpatient encounters but also the clinical information recorded in the physician’s office or the patient’s home during outpatient encounters. Subsets of these records will become part of state and national health care databases. In selecting, therefore, and HIS, the most critical factor is understanding the structure and functionality of its database.

Data Acquisition

The acquisition of clinical data is key to the other functions of the HIS. If the HIS is to support an integrated patient record, then its ability to acquire clinical data from a variety of sources directly affect its ability to support the patient evaluation and management functions described below. All HIS systems provide for direct terminal entry of data. Depending on the system this entry may use only the keyboard or other “point and click” devices together with the keyboard.

Interfaces to other systems will be necessary to compute a complete patient record. The physical interface to those systems is straightforward with today’s technology. The difficulty comes in understand­ing the data that are being transmitted between systems. It is easy to communicate and understand ASCII textual information, but coded information from different systems is generally difficult for sharing between systems. This difficulty results because there are no medical standards for either medical vocab­ulary or the coding systems. Thus, each system may have chose an entirely different terminology or coding system to describe similar medical concepts. In building the interface, therefore, it may be necessary to build unique translation tables to store the information from one system into the databases of the HIS. This requirement has limited the building of truly integrated patient records.

Acquisition of data from patient monitors used in the hospital can either be directly interfaced to the HIS or captured through an interface to an ICU system. Without these interfaces the acquisition of the monitoring data must be entered manually by the nursing personnel. It should be noted that whenever possible automated acquisition of data is preferable to manual entry. The automated acquisition is more accurate and reliable and less resource intensive. With those HISs which do not have interfaces to patient monitors, the frequency of data entry into the system is much less. The frequency of data acquisition affects the ability of the HIS to implement real-time medical decision logic to monitor the status of the patient. That is, in the ICU where decisions need to be made on a very timely manner, the information on which the decision is based must be entered as the critical event is taking place. If there is no automatic entry of the data, then the critical data needed for decision making may not be present, thus preventing the computer from assisting in the management of the patient.

Patient Admission, Transfer, and Discharge Functions

The admission application has three primary functions. The first is to capture for the patient’s computer record pertinent demographic and financial/insurance information. A second function is to communicate that information to all systems existing on the hospital network. The third is to link the patient to previous encounters to ensure that the patient’s longitudinal record is not compromised. This linkage also assists in capturing the demographic and financial data needed for the current encounter, since that information captured during a previous encounter may need not to be reentered as part of this admission. Unfortu­nately in many HISs the linkage process is not as accurate as needed. Several reasons explain this inaccuracy. The first is the motivation of the admitting personnel. In some hospitals they perceive their task as a business function responsible only for ensuring that the patient will be properly billed for his or her hospital stay. Therefore, since the admission program always allows them to create a new record and enter the necessary insurance/billing information, their effort to link the patient to his previous record may not be as exhaustive as needed.

Although the admitting program may interact with many financial and insurance files, there normally exists two key patient files that allow the HIS to meet its critical clinical functions. One is a master patient index (MPI) and the second is the longitudinal clinical file. The MPI contains the unique identifier for the patient. The other fields of this file are those necessary for the admitting clerk to identify the patient. During the admitting process the admitting process the admitting clerk will enter identifying information such as name, sex, birth date, social security number. This information will be used by the program to select potential patient matches in the MPI from which the admitting clerk can link to the current admission. If no matches are detected by the program, the clerk creates a new record in the MPI. It is this process that all too frequently fails. That is, the clerk either enters erroneous data and finds no match or for some reason does not select as a match one of the records displayed. Occasionally the clerk selects the wrong match causing the data from this admission to be posted to the wrong patient. In the earlier HISs where no longitudinal record existed, this problem was not critical, but in today’s system, errors in matching can have serious clinical consequences. Many techniques are being implemented to eliminate this problem including probabilistic matching, auditing processes, postadmission consolidation.

The longitudinal record may contain either a complete clinical record of the patient or only those variables that are most critical in subsequent admissions. Among the data that have been determined as most critical are key demographic data, allergies, surgical procedures, discharge diagnoses, and radiology reports. Beyond these key data elements more systems are beginning to store the complete clinical record. In those systems the structure of the records of the longitudinal file contain information regarding the encounter, admitting physician, and any other information that may be necessary to view the record from an encounter view or as a complete clinical history of the patient.

Patient Evaluation

The second major focus of application development for the HIS is creation of patient evaluation appli­cations. The purpose of these evaluation programs is to provide to the care giver information about the patient which assists in evaluating the medical status of the patient. Depending on the level of data integration in the HIS, the evaluation applications will be either quite rudimentary or highly complex. In the simplest form these applications are departmentally oriented. With this departmental orientation the care giver can access through terminals in the hospital departmental reports. Thus, laboratory reports, radiology reports, pharmacy reports, nursing records, and the like can be displayed or printed at the hospital terminals. This form of evaluation functionality is commonly called results review, since it only allows the results of tests from the departments to be displayed with no attempt to integrate the data from those departments into an integrated patient evaluation report.

The more clinical HISs as mentioned above include a central integrated patient database. With those systems patient reports can be much more sophisticated. A simple example of an integrated patient evaluation report is a diabetic flowsheet. In this flowsheet the caregiver can view the time and amount of insulin given, which may have been recorded by the pharmacy or nursing application, the patient’s blood glucose level recorded in the clinical laboratory or again by the nursing application. In this form the caregiver has within single report, correlated by the computer, the clinical information necessary to evaluate the patient’s diabetic status rather than looking for data on reports from the laboratory system, the pharmacy system, and the nursing application. As the amount and type of data captured by the HIS increases, the system can produce ever-more-useful patient evaluation reports. There exist HISs which provide complete rounds reports the summarize on one to two screens all the patient’s clinical record captured by the system. These reports not only shorten the time need by the caregiver to locate the information, but because of the format of the report, can present the data in a more intuitive and clinically useful form.

Patient Management

Once the caregiver has properly evaluated the state of the patient, the next task is to initiate therapy that ensures an optimal outcome for the patient. The sophistication of the management applications is again a key differentiation of HISs. At the simplest level management applications consist of order-entry applications. The order-entry application is normally executed by a paramedical personnel. That is, the physician writes the order in the patient’s chart, and another person reviews from the chart the written order and enters it into the computer. For example, if the order is for a medication, then it will probably be a pharmacist who actually enters the order into the computer. For most of the other orders a nurse or ward clerk is normally assigned this task. The HIS records the order in the patient’s computerized medical record and transmits the order to the appropriate department for execution. In those hospitals where the departmental systems are interfaced to the HIS, the electronic transmission of the order to the departmental system is a natural part of the order entry system. In many systems the transmission of the order is merely a printout of the order in the appropriate department.

The goal of most HISs is to have the physician responsible for management of the patient enter the orders into the computer. The problem that has troubled most of the HISs in achieving this goal has been the inefficiency of the current order-entry programs. For these programs to be successful they have to complete favorably with the traditional manner in which the physician writes the order. Unfortunately, most of the current order-entry applications are too cumbersome to be readily accepted by the physician. Generally they have been written to assist the paramedic in entering the order resulting with far too many screens or fields that need to be reviewed by the physician to complete the order. One approach that has been tried with limited success is the use of order sets. The order sets have been designed to allow the physician to easily from a single screen enter multiple orders. The use of order sets has improved the acceptability of the order-entry application to the physician, but several problems remain preventing universal acceptance by the physicians. One problem is that the order set will never be sufficiently complete to contain all orders that the physician would want to order. Therefore, there is some subset of patients orders that will have to be entered using the general ordering mechanisms of the program. Depending on the frequency of those orders, the acceptability of the program changes. Maintenance issues also arise with order sets, since it may be necessary to formulate order sets for each of the active physicians. Maintaining of the physician-specific order sets soon becomes a major problem for the data processing department. It becomes more problematic if the HIS to increase the frequency of a given order being present on an order set allows the order sets to be not only physician-defined but problem – oriented as well. Here it is necessary to again increase the number of order sets or have the physicians all agree on those orders to be included in an order set for a given problem.

Another problem, which makes use of order entry by the physician difficult, is the lack of integration of the application into the intellectual tasks of the physician. That is, in most of the systems the physicians are asked to do all the intellectual work in evaluating and managing the care of the patient in the traditional manner and then, as an added task, enter the results of that intellectual effort into the computer. It is at this last step that is perceived by the physician as a clerical task at which the physician rebels. Newer systems are beginning to incorporate more efficiently the ordering task into other appli­cations. These applications assist the physical throughout the entire intellectual effort of patient evaluation and management of the patient. An example of such integration would be the building of evaluation and order sets in the problem list management application. Here when the care provider looks at the patient problem list he or she accesses problem-specific evaluation and ordering screens built into the application, perhaps shortening the time necessary for the physician to make rounds on the patient.

Beyond simple test ordering, many newer HISs are implementing decision support packages. With these packages the system can incorporate medical knowledge usually as rule sets to assist the care provider in the management of patients. Execution of the rule sets can be performed in the foreground through direct calls from an executing application or in the background with the storing of clinical data in the patient’s computerized medical record. This latter mode is called data-driven execution and provides an extremely powerful method of knowledge execution and alerting. that is, after execution of the rule sets, the HIS will “alert” the care provider of any outstanding information that may be important regarding the status of the patient or suggestions on the management of the patient. Several mechanisms have been implemented to direct the alerts to the care provider. In the simplest form notification is merely a process of storing the alert in the patient’s medical record to be reviewed the next time the care provider accesses that patient’s record. More sophisticated notification methods have included directed printouts to indi­viduals whose job it is to monitor the alerts, electronic messages sent directly to terminals notifying the users that there are alerts which need to be viewed, and interfacing to the paging system of the hospital to direct alert pages to the appropriate personnel.

Execution of the rule sets are sometimes, time-driven. This mode results in sets of rules being executed at a particular point in time. The typical scenario for time-driven execution is to set a time of day for selected rule set execution. At that time each day the system executes the given set of rules for a selected population in the hospital. Time drive has proven to be a particularly useful mechanism of decision support for those applications that require hospitalwide patient monitoring.

The use of decision support has ranged from simple laboratory alerts to complex patient protocols. The responsibility of the HIS is to provide the tools for creation and execution of the knowledge base. The hospitals and their designated “experts” are responsible for the actual logic that is entered into the rule sets. Many studies are appearing in the literature suggesting that the addition of knowledge base execution tot he HIS is the next major advancement to e delivered with the HIS. This addition will become a tool to better manage the hospital in the world of managed care.

The inclusion of decision support functionality in the HIS requires that the HIS be designed to support a set of knowledge tools. In general a knowledge bases system will consist of a knowledge base and an inference engine. The knowledge base will contain the rules, frames, and statistics that are used by the inference applications to substantiate a decision. We have found that in the health care area the knowledge base should be sufficiently flexible to support multiple forms of knowledge. That is, no single knowledge representation sufficiently powerful to provide a method to cover all decisions necessary in the hospital setting. For example, some diagnostic decisions may well be best suited for bayesian methods, whereas other management decisions may follow simple rules. In the context of the HIS, I prefer the term application manager to inference engine. The former is intended to imply that different applications may require different knowledge representations as well as different inferencing strategies to traverse the knowledge base. Thus, when the user selects the application, he or she is selecting a particular inference engine that may be unique to that application. The tasks, therefore, of the application manager are to provide the “look and feel” of the application, control the functional capabilities of the application, and invoke the appropriate inference engine for support of any “artificial intelligence” functionality.

175.6 Conclusion

Today’s HIS is no longer the financial/administrative system that first appeared in the hospital. It has extended beyond that role to become an adjunct to the care of the patient. With this extension into clinical care the HIS has not only added new functionality to its design but has enhanced its ability to serve the traditional administrative and financial needs of the hospital as well. The creation of these global applications which go well beyond those of the departmental/clinical systems is now making the HIS the patient – focused system. With this global information the administrators and clinical staff together can accurately access where there are inefficiencies in the operation of the hospital from the delivery of both the administrative and medical care. This knowledge allows changes in the operation of the hospital that will ensure that optimal care continues to be provided to the patient at the least cost to the hospital. These studies and operation changes will continue to grow as the use of an integrated database and implementation of medical knowledge bases become increasingly routine in the functionality of the HIS.

References

Pryor TA, Gardner RM, Clayton PD, et al. 1983. The HELP system. J Med syst 7:213.

Pryor TA, Clayton PD, Haug PJ, et al. 1987. Design of a knowledge driven HIS. Proc. 11th SCAMC, 60.

Bakker AR. 1984. The development of an integrated and co-operative hospital information system. Med Inf 9:135.

Barnett GO. 1984. The application of computer-based medical record systems in ambulatory practice. N Engl J Med 310:1643.

Bleich HL, Beckley RF, Horowitz GL, et al. 1985. Clinical computing in a teaching hospital. N Engl J Med 312:756.

Whiting-O’Keefe QE, Whiting A, Henke J. 1988. The STOR clinical information system. MD Comput 5:8.

Hendrickson G, Anderson RK, Clayton PD, et al.1992. The integrated academic information system at Columbia-Presbyterian Medical Center. MD Comput 9:35.

Safran C, Slack WV, Bleich HL. 1989. Role of computing in patient care in two hospitals. MD Comput 6:141.

Bleich HL, Safran C, Slack WV. 1989. Departmental and laboratory computing in two hospitals. MD Comput 6:149.

ASTM E1238-91. 1992. Specifications for transferring clinical observations between independent computer systems. Philadelphia, American Society for Testing and Materials.

Tierney WM, Miller ME, Donald CJ. 1990. The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests. N Engl J Med 322:1499.

Stead WW, Hammond WE. 1983. Functions required to allow TMR to support the information requirements of a hospital. Proc 7th SCAMC 106.

Safran C, Herrmann F, Rind D, et al. 1990. Computer-based support for clinical decision making. MD Comput 7:319.

Tate KE, Gardner RM, Pryor TA. 1989. Development of a computerized laboratory alerting system. Comp Biomed Res 22:575.

Orthner HF, Blum BI (eds). 1989. Implementing Health Care Information Systems, Springer­Verlag.

Dick RS, Steen EB (eds). 1991. The Computer-Based Patient Record, National Academy Press.

Fitzmaurice, J. M. “Computer-Based Patient Records.” The Biomedical Engineering Handbook: Second Edition. Ed. Joseph D. Bronzino Boca Raton: CRC Press LLC, 2000

Disadvantages

In some cases, digital circuits use more energy than analog circuits to accomplish the same tasks, thus producing more heat. In portable or battery-powered systems this can limit use of digital systems.

For example, battery-powered cellular telephones often use a low-power analog front-end to amplify and tune in the radio signals from the base station. However, a base station has grid power and can use power-hungry, but very flexible software radios. Such base stations can be easily reprogrammed to process the signals used in new cellular standards.

Digital circuits are sometimes more expensive, especially in small quantities.

The sensed world is analog, and signals from this world are analog quantities. For example, light, temperature, sound, electrical conductivity, electric and magnetic fields

Are analog. Most useful digital systems must translate from continuous analog signals to discrete digital signals. This causes quantization errors.

Quantization error can be reduced if the system stores enough digital data to represent the signal to the desired degree of fidelity. The Nyquist-Shannon sampling theorem provides an important guideline as to how much digital data is needed to accurately portray a given analog signal.

In some systems, if a single piece of digital data is lost or misinterpreted, the meaning of large blocks of related data can completely change. Because of the cliff effect, it can be difficult for users to tell if a particular system is right on the edge of failure, or if it can tolerate much more noise before failing.

Digital fragility can be reduced by designing a digital system for robustness. For example, a parity bit or other error management method can be inserted into the signal path. These schemes help the system detect errors, and then either correct the errors, or at least ask for a new copy of the data. In a state-machine, the state transition logic can be designed to catch unused states and trigger a reset sequence or other error recovery routine.

Embedded software designs that employ Immunity Aware Programming, such as the practice of filling unused program memory with interrupt instructions that point to an error recovery routine. This helps guard against failures that corrupt the microcontroller’s instruction pointer which could otherwise cause random code to be executed.

Digital memory and transmission systems can use techniques such as error detection and correction to use additional data to correct any errors in transmission and storage.

On the other hand, some techniques used in digital systems make those systems more vulnerable to single-bit errors. These techniques are acceptable when the underlying bits are reliable enough that such errors are highly unlikely.

• A single-bit error in audio data stored directly as linear pulse code modulation (such as on a CD-ROM) causes, at worst, a single click. Instead, many people use audio compression to save storage space and download time, even though a single-bit error may corrupt the entire song.

Advantages

One advantage of digital circuits when compared to analog circuits is that signals represented digitally can be transmitted without degradation due to noise. For example, a continuous audio signal, transmitted as a sequence of 1s and 0s, can be reconstructed without error provided the noise picked up in transmission is not enough to prevent identification of the 1s and 0s. An hour of music can be stored on a compact disc as about 6 billion binary digits.

In a digital system, a more precise representation of a signal can be obtained by using more binary digits to represent it. While this requires more digital circuits to process the signals, each digit is handled by the same kind of hardware. In an analog system, additional resolution requires fundamental improvements in the linearity and noise charactersitics of each step of the signal chain.

Computer-controlled digital systems can be controlled by software, allowing new functions to be added without changing hardware. Often this can be done outside of the factory by updating the product’s software. So, the product’s design errors can be corrected after the product is in a customer’s hands.

Information storage can be easier in digital systems than in analog ones. The noise – immunity of digital systems permits data to be stored and retrieved without degradation. In an analog system, noise from aging and wear degrade the information stored. In a digital system, as long as the total noise is below a certain level, the information can be recovered perfectly.

Applications of Virtual Instruments in Health Care


Eric Rosow 174.1

Hartford Hospital

Joseph Adam

Premise Development Corporation

Applications of Virtual Instruments in Health Care

Example Application #1 • Example Application #2 • Trending, Relationships, and Interactive Alarms • Data Modeling • Medical Equipment Risk Criteria • Peer Performance Reviews

Applications of Virtual Instruments in Health Care

Virtual Instrumentation (which was previously defined in Chapter 88, “Virtual Instrumentation: Appli­cations in Biomedical Engineering”) allows organizations to effectively harness the power of the PC to access, analyze, and share information throughout the organization. With vast amounts of data available from increasingly sophisticated enterprise-level data sources, potentially useful information is often left hidden due to a lack of useful tools. Virtual instruments can employ a wide array of technologies such as multidimensional analyses and Statistical Process Control (SPC) tools to detect patterns, trends, causalities, and discontinuities to derive knowledge and make informed decisions.

Today’s enterprises create vast amounts of raw data and recent advances in storage technology, coupled with the desire to use this data competitively, has caused a data glut in many organizations. The healthcare industry in particular is one that generates a tremendous amount of data. Tools such as databases and spreadsheets certainly help manage and analyze this data; however databases, while ideal for extracting data are generally not suited for graphing and analysis. Spreadsheets, on the other hand, are ideal for analyzing and graphing data, but this can often be a cumbersome process when working with multiple data files. Virtual instruments empower the user to leverage the best of both worlds by creating a suite of user-defined applications which allow the end-user to convert vast amounts of data into information which is ultimately transformed into knowledge to enable better decision making.

This chapter will discuss several virtual instrument applications and tools that have been developed to meet the specific needs of healthcare organizations. Particular attention will be placed on the use of quality control and “performance indicators” which provide the ability to trend and forecast various metrics. The use of SPC within virtual instruments will also be demonstrated. Finally, a non-traditional application of virtual instrumentation will be presented in which a “peer review” application has been developed to allow members of an organization to actively participate in the Employee Performance Review process.

Example Application #1: The EndoTester™—A Virtual Instrument-Based Quality Control and Technology Assessment System for Surgical Video Systems

The use of endoscopic surgery is growing, in large part because it is generally safer and less expensive than conventional surgery, and patients tend to require less time in a hospital after endoscopic surgery. Industry experts conservatively estimate that about 4 million minimally invasive procedures were per­formed in 1996. As endoscopic surgery becomes more common, there is an increasing need to accurately evaluate the performance characteristics of endoscopes and their peripheral components.

The assessment of the optical performance of laparoscopes and video systems is often difficult in the clinical setting. The surgeon depends on a high quality image to perform minimally invasive surgery, yet assurance of proper function of the equipment by biomedical engineering staff is not always straightfor­ward. Many variables in both patient and equipment may result in a poor image. Equipment variables, which may degrade image quality, include problems with the endoscope, either with optics or light transmission. The light cable is another source of uncertainty as a result of optical loss from damaged fibers. Malfunctions of the charge coupled device (CCD) video camera are yet another source of poor image quality. Cleanliness of the equipment, especially lens surfaces on the endoscope (both proximal and distal ends) are particularly common problems. Patient variables make the objective assessment of image quality more difficult. Large operative fields and bleeding at the operative site are just two examples of patient factors that may affect image quality.

The evaluation of new video endoscopic equipment is also difficult because of the lack of objective standards for performance. Purchasers of equipment are forced to make an essentially subjective decision about image quality. By employing virtual instrumentation, a collaborative team of biomedical engineers, software engineers, physicians, nurses, and technicians at Hartford Hospital (Hartford, CT) and Premise Development Corporation (Avon, CT) have developed an instrument, the EndoTester™, with integrated software to quantify the optical properties of both rigid and flexible fiberoptic endoscopes. This easy-to – use optical evaluation system allows objective measurement of endoscopic performance prior to equip­ment purchase and in routine clinical use as part of a program of prospective maintenance.

The EndoTester™ was designed and fabricated to perform a wide array of quantitative tests and measurements. Some of these tests include: (1) Relative Light Loss, (2) Reflective Symmetry, (3) Lighted (Good) Fibers, (4) Geometric Distortion, and (5) Modulation Transfer Function (MTF). Each series of tests is associated with a specific endoscope to allow for trending and easy comparison of successive measurements.

Specific information about each endoscope (i. e., manufacturer, diameter, length, tip angle, depart­ment/unit, control number, and operator), the reason for the test (i. e., quality control, pre/post repair, etc.), and any problems associated with the scope are also documented through the electronic record. In addition, all the quantitative measurements from each test are automatically appended to the electronic record for life-cycle performance analysis.

Figures 174.1 anD 174.2 illUstrate how information about the fiberoptic bundle of an endoscope can be displayed and measured. This provides a record of the pattern of lighted optical fibers for the endoscope under test. The number of lighted pixels will depend on the endoscope’s dimensions, the distal end geometry, and the number of failed optical fibers. New fiber damage to an endoscope will be apparent by comparison of the lighted fiber pictures (and histogram profiles) from successive tests. Statistical data is also available to calculate the percentage of working fibers in a given endoscope.

In addition to the two-dimensional profile of lighted fibers, this pattern (and all other image patterns) can also be displayed in the form of a three-dimensional contour plot. This interactive graph may be viewed from a variety of viewpoints in that the user can vary the elevation, rotation, size, and perspective controls.

Figure 174.2 ilLustrates how test images for a specific scope can be profiled over time (i. e., days, months, years) to identify degrading performance. This profile is also useful to validate repair procedures by comparing test images before and after the repair.

Applications of Virtual Instruments in Health Care

FIGURE 174.1 Endoscope tip reflection.

Applications of Virtual Instruments in Health Care

FIGURE 174.2 Endoscope profiling module.

The EndoTester™ has many applications. In general, the most useful application is the ability to objectively measure an endoscope’s performance prior to purchase, and in routine clinical use as part of a program of prospective maintenance. Measuring parameters of scope performance can facilitate equip­ment purchase. Vendor claims of instrument capabilities can be validated as a part of the negotiation process. Commercially available evaluation systems (for original equipment manufacturers) can cost upwards of $50,000, yet by employing the benefits of virtual instrumentation and a standard PC, an affordable, yet highly accurate test system for rigid and flexible fiberoptic endoscopes can now be obtained by clinical institutions.

In addition to technology assessment applications, the adoption of disposable endoscopes raises another potential use for the EndoTester™. Disposable scopes are estimated to have a life of 20 to 30 procedures. However, there is no easy way to determine exactly when a scope should be “thrown away.” The EndoTester™ could be used to define this end-point.

The greatest potential for this system is as part of a program of preventive maintenance. Currently, in most operating rooms, endoscopes are removed from service and sent for repair when they fail in clinical use. This causes operative delay with attendant risk to the patient and an increase in cost to the institution. The problem is difficult because an endoscope may be adequate in one procedure but fail in the next which is more exacting due to clinical variables such as large patient size or bleeding. Objective assessment of endoscope function with the EndoTester™ may eliminate some of these problems.

Equally as important, an endoscope evaluation system will also allow institutions to ensure value from providers of repair services. The need for repair can be better defined and the adequacy of the repair verified when service is completed. This ability becomes especially important as the explosive growth of minimally invasive surgery has resulted in the creation of a significant market for endoscope repairs and service. Endoscope repair costs vary widely throughout the industry with costs ranging from $500 to $1500 or more per repair. Inappropriate or incomplete repairs can result in extending surgical time by requiring the surgeon to “switch scopes” (in some cases several times) during a surgical procedure.

Given these applications, we believe that the EndoTester™ can play an important role in reducing unnecessary costs, while at the same time improving the quality of the endoscopic equipment and the outcome of its utilization. It is the sincere hope of the authors that this technology will help to provide accurate, affordable and easy-to-acquire data on endoscope performance characteristics which clearly are to the benefit of the healthcare provider, the ethical service providers, manufacturers of quality products, the payers, and, of course, the patient.

Example Application #2: PIVIT™—Performance Indicator Virtual Instrument Toolkit

Most of the information management examples presented in this chapter are part of an application suite called PIVIT™. PIVIT is an acronym for “Performance Indicator Virtual Instrument Toolkit” and is an easy-to-use data acquisition and analysis product. PIVIT was developed specifically in response to the wide array of information and analysis needs throughout the healthcare setting.

PIVIT applies virtual instrument technology to assess, analyze, and forecast clinical, operational, and financial performance indicators. Some examples include applications which profile institutional indi­cators (i. e., patient days, discharges, percent occupancy, ALOS, revenues, expenses, etc.), and departmen­tal indicators (i. e., salary, non-salary, total expenses, expense per equivalent discharge, DRGs, etc.). Other applications of PIVIT include 360° Peer Review, Customer Satisfaction Profiling, and Medical Equipment Risk Assessment.

PIVIT can access data from multiple data sources. Virtually any parameter can be easily accessed and displayed from standard spreadsheet and database applications (i. e., Microsoft Access, Excel, Sybase, Oracle, etc.) using Microsoft’s Open Database Connectivity (ODBC) technology. Furthermore, multiple parameters can be profiled and compared in real-time with any other parameter via interactive polar plots and three-dimensional displays. In addition to real-time profiling, other analyses such as SPC can

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Be employed to view large data sets in a graphical format. SPC has been applied successfully for decades to help companies reduce variability in manufacturing processes. These SPC tools range from Pareto graphs to Run and Control charts. Although it will not be possible to describe all of these applications, several examples are provided below to illustrate the power of PIVIT.

Trending, Relationships, and Interactive Alarms

Figure 174.3 Illustrates a virtual instrument that interactively accesses institutional and department specific indicators and profiles them for comparison. Data sets can be acquired directly from standard spreadsheet and database applications (i. e., Microsoft Access®, Excel®, Sybase®, Oracle®, etc.). This capability has proven to be quite valuable with respect to quickly accessing and viewing large sets of data. Typically, multiple data sets contained within a spreadsheet or database had to be selected and then a new chart of this data had to be created. Using PIVIT, the user simply selects the desired parameter from any one of the pull-down menus and this data set is instantly graphed and compared to any other data set.

Interactive “threshold cursors” dynamically highlight when a parameter is over and/or under a specific target. Displayed parameters can also be ratios of any measured value, for example, “Expense per Equiv­alent Discharge” or “Revenue to Expense Ratio”. The indicator color will change based on how far the data value exceeds the threshold value (i. e., from green to yellow to red). If multiple thresholds are exceeded, then the entire background of the screen (normally gray) will change to red to alert the user of an extreme condition.

Finally, multimedia has been employed by PIVIT to alert designated personnel with an audio message from the personal computer or by sending an automated message via e-mail, fax, pager, or mobile phone.

PIVIT also has the ability to profile historical trends and project future values. Forecasts can be based on user-defined history (i. e., “Months for Regression”), the type of regression (i. e., linear, exponential, or polynomial), the number of days, months, or years to forecast, and if any offset should be applied to the forecast. These features allow the user to create an unlimited number of “what if” scenarios and allow only the desired range of data to be applied to a forecast. In addition to the graphical display of data values, historical and projected tables are also provided. These embedded tables look and function very much like a standard spreadsheet.

Applications of Virtual Instruments in Health Care

FIGURE 174.4 Injury epidemiology and public policy knowledgebase.

Data Modeling

Figure 174.4 Illustrates another example of how virtual instrumentation can be applied to financial modeling and forecasting. This example graphically profiles the annual morbidity, mortality, and cost associated with falls within the state of Connecticut. Such an instrument has proved to be an extremely effective modeling tool due to its ability to interactively highlight relationships and assumptions, and to project the cost and/or savings of employing educational and other interventional programs.

Virtual instruments such as these are not only useful with respect to modeling and forecasting, but perhaps more importantly, they become a “knowledgebase” in which interventions and the efficacy of these interventions can be statistically proven. In addition, virtual instruments can employ standard technologies such as Dynamic Data Exchange (DDE), ActiveX, or TCP/IP to transfer data to commonly used software applications such as Microsoft Access® or Microsoft Excel®. In this way, virtual instruments can measure and graph multiple signals while at the same time send this data to another application which could reside on the network or across the Internet.

Another module of the PIVIT application is called the “Communications Center.” This module can be used to simply create and print a report or it can be used to send e-mail, faxes, messages to a pager, or even leave voice-mail messages. This is a powerful feature in that information can be easily and efficiently distributed to both individuals and groups in real-time.

Additionally, Microsoft Agent® technology can be used to pop-up an animated help tool to commu­nicate a message, indicate an alarm condition, or can be used to help the user solve a problem or point out a discrepancy that may have otherwise gone unnoticed. Agents employ a “text-to-speech” algorithm to actually “speak” an analysis or alarm directly to the user or recipient of the message. In this way, on­line help and user support can also be provided in multiple languages.

In addition to real-time profiling of various parameters, more advanced analyses such as SPC can be employed to view large data sets in a graphical format. SPC has been applied successfully for decades to help companies reduce variability in manufacturing processes. It is the opinion of this author that SPC has enormous applications throughout healthcare. For example, Fig. 174.5 sHows how Pareto analysis can be applied to a sample trauma database of over 12,000 records. The Pareto chart may be frequency or percentage depending on front panel selection and the user can select from a variety of different parameters by clicking on the “pull-down” menu. This menu can be configured to automatically display

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Each database field directly from the database. In this example, various database fields (i. e., DRG, Principal Diagnosis, Town, Payer, etc.) can be selected for Pareto analysis. Other SPC tools include run charts, control charts, and process capability distributions.

Medical Equipment Risk Criteria

Figure 174.6 iLlustrates a virtual instrument application which demonstrates how four “static” risk categories (and their corresponding values) are used to determine the inclusion of clinical equipment in the Medical Equipment Management Program at Hartford Hospital. Each risk category includes specific sub-categories that are assigned points, which when added together according to the formula listed below, yield a total score which ranges from 4 to 25.

Considering these scores, the equipment is categorized into five priority levels (High, Medium, Low, Grey List, and Non-Inclusion into the Medical Equipment Management Program). The four static risk categories are:

Equipment Function (EF): Stratifies the various functional categories (i. e., therapeutic, diagnostic, analytical, and miscellaneous) of equipment. This category has “point scores” which range from 1 (miscellaneous, non-patient related devices) to 10 (therapeutic, life support devices).

Physical Risk (PR): Lists the “worst case scenario” of physical risk potential to either the patient or the operator of the equipment. This category has “point scores” which range from 1 (no significant identified risk) to 5 (potential for patient and/or operator death).

Environmental Use Classification (EC): Lists the primary equipment area in which the equipment is used and has “point scores” which range from 1 (non-patient care areas) to 5 (anesthetizing locations).

Preventive Maintenance Requirements (MR): Describes the level and frequency of required mainte­nance and has “point scores” which range from 1 (not required) to 5 (monthly maintenance).

Applications of Virtual Instruments in Health Care

FIGURE 174.6 Medical equipment risk classification profiler.

The Aggregate Static Risk Score is calculated as follows:

Aggregate Risk Score = EF + PR + EC + MR

Using the criteria’s system described above, clinical equipment is categorized according to the following priority of testing and degree of risk:

High Risk: Equipment that scores between and including 18 to 25 points on the criteria’s evaluation system. This equipment is assigned the highest risk for testing, calibration, and repair.

Medium Risk: Equipment that scores between and including 15 to 17 points on the criteria’s evaluation system.

Low Risk: Equipment that scores between and including 12 to 14 points on the criteria’s evaluation system.

Hazard Surveillance (Gray): Equipment that scores between and including 6 and 11 points on the criteria’s evaluation system is visually inspected on an annual basis during the hospital hazard surveillance rounds.

Medical Equipment Management Program Deletion: Medical equipment and devices that pose little risk and scores less than 6 points may be deleted from the management program as well as the clinical equipment inventory.

Future versions of this application will also consider “dynamic” risk factors such as: user error, mean- time-between failure (MTBF), device failure within 30 days of a preventive maintenance or repair, and the number of years beyond the American Hospital Association’s recommended useful life.

Peer Performance Reviews

The virtual instrument shown in Fig. 174.7 haS been designed to easily acquire and compile performance information with respect to institution-wide competencies. It has been created to allow every member of a team or department to participate in the evaluation of a co-worker (360° peer review). Upon running the application, the user is presented with a “Sign-In” screen where he or she enters their username and

Applications of Virtual Instruments in Health Care

FIGURE 174.7 Performance reviews using virtual instrumentation.

Password. The application is divided into three components. The first (top section) profiles the employee and relevant service information. The second (middle section) indicates each competency as defined for employees, managers, and senior managers. The last (bottom) section allows the reviewer to evaluate performance by selecting one of four “radio buttons” and also provide specific comments related to each competency. This information is then compiled (with other reviewers) as real-time feedback.

References

American Society for Quality Control. American National Standard. Definitions, Symbols, Foru –

Mulas, and Tables for Control Charts, 1987.

Breyfogle, F. W., Statistical Methods for Testing, Development and Manufacturing, John Wiley & Sons, New York, 1982.

Carey, R. G. and Lloyd, R. C. Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications, 1995.

Fennigkow, L. and Lagerman, B., Medical Equipment Management. 1997 EC/PTSM Series/No. 1; Joint Commission on Accreditation of Hospital Organizations, 1997; 47-54.

Frost & Sullivan Market Intelligence, file 765, The Dialog Corporation, Worldwide Headquarters,

2440 W. El Camino Real, Mountain View, CA 94040.

Inglis, A., Video Engineering, McGraw Hill, New York, 1993.

Kutzner J., Hightower L., and Pruitt C. Measurement and Testing of CCD Sensors and Cameras, SMPTE Journal, pp. 325-327, 1992.

Measurement of Resolution of Camera Systems, IEEE Standard 208, 1995.

Medical Device Register 1997, Volume 2, Montvale NJ, Medical Economics Data Production Company, 1997.

Montgomery, D. C., Introduction to Statistical Quality Control, 2nd ed., John Wiley & Sons, 1992.

Rosow, E., Virtual Instrumentation Applications with BioSensors, presented at the Biomedical Engineering Consortium for Connecticut (BEACON) Biosensor Symposium, Trinity College, Hart­ford, CT, October 2, 1998.

Rosow, E., Adam, J., and Beatrice, F., The EndoTester™: A Virtual Instrument Endoscope Evalu­ation System for Fiberoptic Endoscopes, Biomedical Instrumentation and Technology, pp. 480-487, September/October 1998.

Surgical Video Systems, Health Devices, 24(11), 428-457, 1995.

Walker, B., Optical Engineering Fundamentals, McGraw Hill, New York, 1995.

Wheeler, D. J. and Chambers, D. S., Understanding Statistical Process Control, SPC Press, 2nd ed., 1992.

Kun, L. G. “Medical Informatics.”

The Biomedical Engineering Handbook: Second Edition.

Ed. Joseph D. Bronzino

Boca Raton: CRC Press LLC, 2000

XVIII

Medical Informatics

Luis G. Kun

CIMIC/Rutgers University

Hospital Information Systems: Their Function and State T Allan Pryor Patient Database Strategies for the HIS • Data Acquisition • Patient Admission, Transfer, and Discharge Functions • Patient Evaluation • Patient Management • Conclusion

Computer-Based Patient Records J. Michael Fitzmaurice Computer-Based Patient Record • Clinical Decision Support Systems • Scientific Evidence • Hospital and Ambulatory Care Systems • Extended Uses of CPR Data • Selected Issues

Computer Networks in Health Care Soumitra Sengupta

History • Impact of Clinical Data • Information Types • Platforms • Current Technologies • Conclusions

Overview of Standards Related to the Emerging Health Care Information Infrastructure Jeffrey S. Blair

Identifier Standards • Communications (Message Format) Standards • Content and Structure Standards • Clinical Data Representations (Codes) • Confidentiality, Data Security, and Authentication • Quality Indicators and Data Sets • International Standards • Standards Coordination and Promotion Organizations • Summary

Non-AI Decision Making Ron Summers, Derek G. Cramp, Ewart R. Carson Analytical Models • Decision Theoretic Models • Statistical Models • Summary

Design Issues in Developing Clinical Decision Support and Monitoring Systems John W. Goethe, Joseph D. Bronzino

Design Recommendations • Description of a Clinical Monitoring System • Outcome Assessment • Conclusion

I

N THE LAST 20 YEARS the field of medical informatics has grown tremendously both in its complexity and in content. As a result, two sections will be written in this Handbook. The first one, represented in the chapters, will be devoted to areas that form a key “core” of computer technologies. These include: hospital information systems (HIS), computer-based patient records (CBPR or CPR), imaging, communications, standards, and other related areas. The second section includes the following topics: artificial intelligence, expert systems, knowledge-based systems neural networks, and robotics. Most of the techniques describe in the second section will require the implementation of systems explained in this first section. We could call most of these chapters the information infrastructure required to apply medical informatics’ techniques to medical data. These topics are crucial because they not only lay the foundation required to treat a patient within the walls of an institution but they provide the roadmap required to deal with the patient’s lifetime record while allowing selected groups of researchers and clinicians to analyze the information and generate outcomes research and practice guidelines information.

As an example a network of associated hospitals in the East Coast (a health care provider network) may want to utilize an expert system that was created and maintained at Stanford University. This group of hospitals, HMOs, clinics, physician’s offices, and the like would need a “standard” computer-based patient record (CPR) that can be used by the clinicians from any of the physical locations. In addition in order to access the information all these institutions require telecommunications and networks that will allow for the electronic “dialogue.” The different forms of the data, particularly clinical images, will require devices for displaying purposes, and the information stored in the different HIS, Clinical Infor­mation Systems (CIS), and departmental systems needs to be integrated. The multimedia type of record would become the data input for the expert system which could be accessed remotely (or locally) from any of the enterprise’s locations. On the application side, the expert system could provide these institu­tions which techniques that can help in areas such as diagnosis and patient treatment. However, several new trends such as: total quality management (TQM), outcome research and practice guidelines could be followed. It should be obvious to the reader that to have the ability to compare information obtained in different parts of the world by dissimilar and heterogeneous systems, certain standards need to be followed (or created) so that when analyzing the data the information obtained will make sense.

Many information systems issues described in this introduction will be addressed in this section. The artificial intelligence chapters which follow should be synergistic with these concepts. A good under­standing of the issues in this section is required prior to the utilization of the actual expert system. These issues are part of this section of medical informatics, other ones, however, e. g., systems integration and process reengineering, will not be addressed here in detail but will be mentioned by the different authors. I encourage the reader to follow up on the referenced material at the end of each chapter, since the citations contain very valuable information.

Several current perspectives in information technologies need to be taken in consideration when reading this section. One of them is described very accurately in the book entitled Globalization, Tech­nology and Competition (The Fusion of Computers and Telecommunications in the 1990s) by Bradley, Hausman and Nolan (1993). The first chapter of this book talks about new services being demanded by end users which include the integration of computers and telecommunications. From their stages theory point of view, the authors describe that we are currently nearing the end of the micro era and the beginning of the network era. From an economy point of view, the industrial economy (1960s and 1970s) and the transitional economy (1970s and 1980s) is moving into an information economy (1990s and beyond). Also a leadership survey done in 1994 by the Healthcare Information and Management System Society (HIMSS) on trends in health care computing to mainly chief information officers, directors, and the likes of health care providers showed the following results:

In a market driven by cost containment, the most important forces driving increased computer­ization in health care were: (a) movement to manage care (25%), (b) outcomes data requests (24%), and (c) movement to health care networks (17%).

The most important systems priority for the next 2 years: (a) integrating across separate facilities (31%), (b) implementing a computer-based patient record (CPR) (19%), and (c) integrating departmental systems (13%) and reengineering to patient focused care (13%).

56% felt that the information superhighway was essential for health care.

In the next 3 years the most significant health-care related computer development affecting the average consumer would be (a) more streamlined health care encounters (49%), (b) access to health information/services from home (20%), and (c) health care “smartcards” (17%).

Although 49% claimed to use the Internet, their health care facilities are using it for: (a) point – to-point E-mail (81%), (b) clinicians querying research databases (69%), (c) consumer-provider exchange (31%), and (d) two-way medical consultations (22%).

Clinicians will share computerized patient information in a nationwide system: (a) by the year 2000 (39%), (b) not happen for at least ten years (38%), and (c) within 1 to 3 years (14%). Many other questions and answers reflected some of the current technological barriers and users needs. Because of these trends it was essential to include in this Handbook technologies that today may be considered state of the art but when read about 10 years from now will appear to be transitional only. Information technologies are moving into a multimedia environment which will require special techniques for acquiring, displaying, storing, retrieving, and communicating the informa­tion. We are in the process of defining some of these mechanisms.

In some instances such as imaging, this Handbook contains a full section dedicated to the subject. That section contains the principles, the associated math algorithms, and the physics related to all medical imaging modalities. The intention in this section is to address issues related to imagining as a form of medical information. These concepts include issues related then to acquisition, storage/retrieval, display and communications of document and clinical images, e. g., picture archival and communications systems (PACS). From a CPR point of view, clinical and document images will become part of this electronic chart, therefore many of the associated issues will be discussed in this section more extensively.

The state of the telecommunications has been described as a revolution; data and voice communica­tions as well s full-motion video have come together as a new dynamic field. Much of what is happening today is a result of technology evolution and need. The connecting thread between evolutionary needs and revolutionary ideas is an integrated perspective of both sides of multiple industries. This topic will also be described in more detail in this section.

In the first chapter Allan Pryor provides us with a tutorial on hospital information systems (HIS). He describes not only the evolution of HIS and departmental systems and clinical information systems (CIS), but also their differences. Within the evolution he follows these concepts with the need for the longitudinal patient record and the integration of patient data. This chapter includes patient database strategies for the HIS, data acquisition, patient admission, transfer and discharge functions. Also discussed are patient evaluation and patient management issues. From an end-user point of view, a terrific description on the evolution of data-driven and time-driven systems is included, culminating with some critical concepts on HIS requirements for decision support and knowledge base functionality. His conclusions are good indication of his vision.

Michael Fitzmaurice follows with “Computer-Based Patient Records” (CBPR or CPR). In the intro­duction, it is explained what is the CPR and way it is a necessary tool for supporting clinical decision making and how it is enhanced when it interacts with medial knowledge sources. This is followed by clinical decision support systems (CDSS): knowledge server, knowledge sources, medical logic modules (MLM), and nomenclature. This last issue in particular is one which needs to be well understood. The nomenclature used by physicians and by the CPRs differ among institutions. Applying logic to the wrong concepts can produce misinterpretations. The scientific evidence in this chapter includes patient care process, CDSS hurdles, CPR implementation, research data bases, telemedicine, hospital and ambulatory care systems. A table of hospital and ambulatory care computer-based patient records systems concludes this chapter.

Today it is impossible to separate computers and telecommunications (communications and net­works). Both are part of information systems. Soumitra Sengupta provides us n this chapter with a tutorial-like presentation which includes an introduction and history, impact of clinical data, information types, and platforms. The importance of this section is reflected both in the contents reviewed under current technologies—LANs, WANs, middleware, medical domain middleware; integrated patient data base, and medical vocabulary—as well as in the directions and challenges section which includes improved bandwidth, telemedicine, and security management. In the conclusions the clear vision is that networks will become the de facto fourth utility after electricity, water, and heat.

“Non-AI Decision Making” is covered by Ron Summers and Ewart Carson. This chapter includes an introduction which explains the techniques of procedural or declarative knowledge. The topics covered in this section include: analytical models, and decision theoretic models, including clinical algorithms and decision trees. The section that follows cover a number of key topics which appear while querying large clinical databases to yield evidence of either diagnostic/treatment or research value; statistical models, database search, regression analysis, statistical pattern analysis, bayesian analysis, Depster-Shafer theory, syntactic pattern analysis, causal modeling, artificial neural networks. In the summary the authors clearly advise the reader to read this section in conjunction with the expert systems chapters that follow.

The standards section is closely associated with the CPR chapter of this section. Jeff Blair does a terrific job with his overview of standards related to the emerging health care information infrastructure. This chapter should give the reader not only an overview of the major existing and emerging health care information standards but an understanding of all current efforts, national and international, to coordi­nate, harmonize, and accelerate these activities. The introduction summarizes how this section is orga­nized. It includes identifier standards (patient’s, site of care, product, and supply labeling), communications (message format) standards, content and structure standards. This section is followed by a summary of clinical data representations, guidelines for confidentiality, data, security, and authentication. After that quality indicators and data sets are described along with international standards. Coordinating and promotion organizations are listed at the end of this chapter including points of contact which will prove very beneficial for those who need to follow up.

Design issues in developing clinical decision support and monitoring systems by John Goethe and Joseph Bronzino provide insight for the development of clinical decision support systems. In their intro­duction and throughout this chapter the authors provide a step-by-step tutorial with practical advice and make recommendations on design of the systems to achieve end-user acceptance. After that a description of a clinical monitoring system, developed and implemented by them for a psychiatric practice, is presented in detail. In their conclusions the human engineering issue is discussed.

The authors of this section represent industry, academia and government. Their expertise in many instances is multiple from developing to actual implementing these technical ideas. I am very grateful for all our discussions and their contributions.

Pryor, T. A. “Hospital Information Systems: Their Function and State.” The Biomedical Engineering Handbook: Second Edition.

Ed. Joseph D. Bronzino

Boca Raton: CRC Press LLC, 2000