- Email: [email protected]
Towards the development of computerised healthcare delivery in the hospital setting
Control Engineering Practice 10 (2002) 111–117
Towards the development of computerised healthcare delivery in the hospital setting M.A. Hughesa,*, E.R. Carsona, M.A. Makhlouf b,a, C.J. Morganc,a, R. Summersd,a a
Centre for Management and Information in Medicine, City University, London, UK b MITRE Corporation, Bedford, Massachusetts, USA c Department of Anaesthesia, Royal Brompton and Harefield NHS Trust, London, UK d Department of Information Science, Loughborough University, Loughborough, UK Received 4 September 2000; accepted 14 February 2001
Abstract This paper discusses issues relating to the development and evaluation of the next generation of computerised control systems in healthcare delivery. It will be argued that further increases in the cost-effectiveness of healthcare delivery and the successful implementation of organisational learning models of healthcare can best be achieved if computer systems are developed which go beyond the data management functions that typify current systems. It is further argued that the evaluation parameters of such systems are more fundamental and wide reaching than those normally employed. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Biomedical control; Control system analysis; Evaluation; Information systems; Requirements analysis
1. Introduction The clinical information systems considered in this paper are those used to make healthcare resource allocation decisions, i.e. decisions as to which patients should receive which resource inputs (therapeutic interventions), and when. As such a clinical information system can be implemented using a combination of human and computer-based functionality. Historically, however, the computerised element of clinical information systems has consisted of the simple data management functions of data collection, storage and dissemination, with the more complex analysis and decision-making functions, being undertaken by the human componentFthe userFof the system. The purpose of this paper is to model two very general healthcare processes and examine the opportunities they provide for making further advances in the computerisation of healthcare delivery and the advantages accruing thereof. These two processes are both types of healthcare resource allocation process as they occur in a hospital setting, although the conclusions may be applied to any other healthcare setting where those *Corresponding author. E-mail address: [email protected] (M.A. Hughes).
processes occur. The first is that of determining patients’ general resourcing requirements. That is, determining which units within a hospital the patient will be admitted to, and when. The second is determining the patients’ specific resourcing requirements in terms of the therapeutic interventions that are necessary once admitted to a unit. It will be argued that, for both types of resource allocation process, the adoption of various modern patient management paradigms and the fulfillment of resource utilisation targets can only be realistically achieved through the development of systems whose computerised functionality goes beyond that of the simple data management functions that typify current computerised information systems. It will be further argued that many of the advantages distinct to computerised implementations of organisational processes are more fundamental and wide-reaching than those parameters which are normally employed in making comparisons between human-based and machine-based implementations of the same organisational process. The approach that will be adopted is to develop in outline the implementation-independent operational model of an information system for each resource allocation process, referred to hereafter as the schedul-
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ing function and the treatment function. Each function will be modelled separately and evaluated in terms of its ability to satisfy the identified requirements. The requirements are discussed in broad qualitative terms from within a control-theoretic and object-oriented framework. Implementation issues will be considered in terms of whether the component informational processes of each function are best implemented in a computer- or human-based system. The structure of this paper is as follows. In Sections 2 and 3, the two outline operational models are presented. In Section 2, the scheduling function model is presented; in Section 3, the treatment function model. In Section 4, issues involving the implementation of the component processes of each function are discussed. In this discussion, attention will be focussed on the question of what advantages there are in computerisation over the existing human-based implementation. In Section 5, a general discussion of the issues raised in the preceding sections is given. This discussion will develop further the themes of Section 4 in an attempt to gauge the impact of greater computerisation on the delivery of healthcare. Finally, in Section 6, the conclusions are summarised and consideration given to the development of the next generation of clinical information systems. This is achieved through a brief description of a system, currently being developed at the Royal Brompton and Harefield NHS Trust, London that implements some of the functionality that will be discussed here.
2. Scheduling function The scheduling function is the process of determining patients’ general resourcing requirements in terms of which hospital units the patient will be admitted to and when. This process usually involves three distinct steps: 1. Determining which resources are or will be available and at what times in which component units of the healthcare facility; 2. Determining which patients require admission and at what times to which component units of the healthcare facility; and 3. Allocating available resources to patients queuing for admission in such a way as to optimise costeffectiveness. The term ‘available resources’ refers to a set of resources that are conjointly sufficient to provide an appropriate level of therapy to an admitted patient. These resources will vary depending on the type of patient admitted, although with most healthcare units admitting only patients with a certain range of diagnoses or levels of severity, the resource inputs required may be considered as a generic package per unit of time, and shall be referred to here as a bed-slot.
In the case of a stand-alone healthcare unit where there is no staged delivery of treatment involving the transfer of patients to or from other healthcare units, assigning patients to available bed-slots is a relatively simple process. However, it becomes much more difficult in the case of progressive-care systems, where the delivery of healthcare is staged according to functionally specialised and interdependent healthcare units. For example, a patient may first be admitted to an operating room, and then subsequently admitted to another unit, such as a post-operative recovery room, and then maybe to another unit such as an intensive-care unit, and so on until the patient is finally discharged. This interdependence between the different units of a progressive-care system means that a scheduler must allocate bed-slots to a patient in several different units simultaneously. Moreover, this has to be accomplished in such a way as to coincide with the patient’s anticipated movement through the system, and the patient’s anticipated length of stay in each unit within the system, both of which may change according to deviations from the individual patient’s expected course of recovery. Put simply, the scheduler must be able to predict the consumption of bed-slots before the consumption can be scheduled. To be able to optimise the scheduling process, two pieces of information are needed: the number of bedslots available in each unit within the scheduling period, and the number of bed-slots in each unit over that period that are required. In the case of a progressivecare system this can be derived from the following variables (for stand-alone facilities, the variables 1b and 2b do not apply): 1.
2.
3.
For patients queuing for admission to the system: (a) Degree of urgency (b) To which units they require admission, and in what order (c) The length of time they will stay in each unit For patients already admitted to the system: (a) Their current location (b) To which subsequent units they will require admission, and in what order (c) The length of time they will stay in each unit, including the current one For each unit: (a) The total number of bed-slots for each unit of time within the scheduling period.
From the above list, items 1a–c may be used to derive the demand for available bed-slots in each unit over time, and items 2a–c and 3a may be used to derive the supply of available bed-slots in each unit over time. The processing involved in the scheduling function as it occurs in a surgical facility is shown in Fig. 1 below. In this diagram, an object-oriented approach is assumed, with actors being represented by clear boxes and the
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Surgeon CreateNew (patient)
Scheduler Schedule (patient)
Clinician UpdateOld (patient)
UpdateOld (bed-slot)
CreateNew (bed-slot)
AllocateNew (bed-slot)
Scheduler AllocateNew (patient)
Fig. 1. Activity diagram of the scheduling function before computerisation.
activities which they perform being represented by shaded boxes labelled by the name of the activity and the object class of which it is a component. Reading the diagram from top to bottom, the actor Surgeon begins by creating a new patient (‘CreateNew(patient)’) in so doing predicting the information items 1a–c from the above list. After a new patient has been created, the actor Scheduler begins the scheduling process (‘Schedule(patient)’) which amounts to placing the new patient onto a list for admission to the healthcare facility. The Actor Clinician then updates the information items 2a–c from the above list (‘UpdateOld(patient)’) and consequently updates the corresponding data on the resources which have already been allocated (‘UpdateOld(bed-slot)’). Scheduler then determines which resources are available to be allocated to patients queuing for admission (‘CreateNew(bed-slot)’), after which available resources may be allocated to queuing patients (‘AllocateNew(bed-slot)’) and viceversa (‘AllocateNew(patient)’). Of the activities involved in the scheduling function shown above, it is proposed here that the computerisation of the activities UpdateOld(patient) and UpdateOld(bed-slot) is possible through the introduction of a new actor called Expert System as shown in Fig. 2. The extent to which the variables 1b,c and 2b,c may be predicted by a computerised expert system on the basis of an evaluation of the patient’s clinical and demographic characteristics remains an open question (see, e.g., Tu & Mazer, 1996). However, many clinical prediction algorithms which have been developed have shown a level of accuracy which approaches or surpasses that of a human predictor (see, e.g. Barie, Hydo, & Fischer, 1996; Buchman, Kubos, & Seidler, 1994; Watts & Knaus, 1994). There is also evidence that
it is possible to predict with a high-degree of accuracy the likelihood of unit discharge over the succeeding 24 h, which suggests that a system of updating should be in place to reflect the dynamics of the patient’s physiology (Marshall, Sibbald, & Cock, 1995). Moreover, it will be argued below that the accuracy of the prediction is only one parameter, which needs to be considered when evaluating different implementations of the same process.
3. Treatment function There has recently been much discussion around various paradigms in patient management such as evidence-based medicine, clinical guidelines, the use of critical pathways, and so on, and in particular how they may be best implemented in the clinical setting (Davis & Taylor-Vaisey, 1997; Woolf, 1992). It is argued here that what is common to all of these paradigms is the need for an informational infrastructure to support their implementation. More specifically, these paradigms are looking upon the process of patient management from a control theoretic perspective and there needs to be developed clinical information systems that are able to support this. Adopting the control-theoretic perspective, such an information system needs three pieces of information for each controlled clinical variable: 1. The present value of the variable; 2. The ideal value of the variable, and; 3. The treatment needed to make the present value the ideal value. The procedural aspects of patient management are shown in Fig. 3. In this figure, the actor Clinician
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Surgeon CreateNew ( patient)
Scheduler Schedule ( patient)
Expert System
UpdateOld ( patient)
UpdateOld (bed-slot)
CreateNew (bed-slot)
AllocateNew (bed-slot)
Scheduler AllocateNew ( patient)
Fig. 2. Activity diagram of the scheduling function after computerisation.
Clinician UpdateOld ( patient)
Benchmark ( patient)
Treat ( patient)
Fig. 3. Activity diagram of the treatment function before computerisation.
starts updating the values for each controlled variable for the patient (‘UpdateOld(patient)’). Then the clinician calculates what the value for each controlled variable should be (‘Benchmark(patient)’). That is, for each variable, calculate what the ideal value would be for the patient, given the patient’s age, operative category, diagnosis, and so on. With this information the clinician can then treat the patient with the goal of reducing the difference between the present value and the benchmarked value for each controlled variable (‘Treat(patient)’). Of the activities involved in the treatment function shown above, it is proposed here that the computerisation of the benchmarking activity Benchmark(patient) is possible through the introduction of a new actor called Benchmarking System as shown in Fig. 4. The extent to which the ideal value of a variable can be algorithmically predicted given a patient’s clinical and demographic characteristics is, as with the prediction of length of stay and admissions requirements above, an open question. However, some recent studies (for example, Zimmerman, Wagner, Draper, & Knaus, 1994; Seneff, Zimmerman, & Knaus, 1996) have shown that such predictions can be made for at least some important variables, and that the making of such predictions can have a beneficial impact on patient management. Again, however, as with the introduction of the expert system in the scheduling function, it will be argued below that the accuracy of these predictions is only one amongst many evaluation parameters.
4. Implementation When one thinks of the basis for comparing computerised clinical prediction with human-based prediction, the immediate reaction is to compare them on the basis of the accuracy of the predictions that they generate. This is an understandable reaction, and if the comparison were made ceteris paribus, it would be a reasonable one. However, when a computer system is implemented all other things do not remain unchanged since new possibilities for enhancing the level of control over the disease process are introduced which were either not previously possible with a human-based system of prediction, or simply not cost-effective. The purpose of this section is to identify those parameters which may change consequent to the proposed computerisation of the scheduling and treatment functions described above. In particular, three parameters are identified: processing capacity, processing transparency and processing standardisation. These parameters will be discussed in turn. 4.1. Processing capacity With regard to frequency of measurement of the variables, items 2a, 3a of the scheduling function, and items 2 and 3 of the treatment function need be measured relatively infrequently. The other variables, however, are constantly changing because the patient’s underlying physiology from which all those variables are
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Expert System
UpdateOld ( patient)
Benchmarking System
Expert System
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Benchmark ( patient)
Treat (patient)
Fig. 4. Activity diagram of the treatment function after computerisation.
at least partly derived is constantly changing. Thus, the frequency of updating of this information determines the extent to which the system can be controlled. The more frequently a variable is measured, the more control the scheduler can exert over the progressive-care system or the clinician can exert over a patient’s therapy, and hence the more likely that performance will be optimised in each case. This requirement places a great burden on an implemented information system in terms of data requirements that only a computerised implementation can realistically accomplish. Consider the quantity of information involved. For example, the prediction of length of stay (items 1c and 2c of the scheduling function) is made on the basis of various physiological, therapeutic, and demographic parameters such as age, blood pressure, operative category, and so forth. If one makes a conservative estimate of there being 10 data entries for each prediction, 5 of which correspond to dynamic underlying physiological variables. If one further assumes that predictions need to be updated every hour, for a total of 50 patients, then it soon becomes impractical, or at least not cost-effective, for a human-based data collection system to be implemented. Likewise for the measurement of physiological variables in the treatment function, where updating may need to be even more frequent, every minute or every second. Thus, insofar that a computerised system is able to cost-effectively provide a greater proportion of the information which is needed than a human-based system, one is able to say that computerisation offers greater scope for control. 4.2. Processing transparency In the case of the prediction of clinical variables, there is no reason to assume that machine prediction is any better or worse than human (i.e., clinician) prediction or benchmarking (see, for example, Mounsey, Griffith, Heaviside, Hedley Brown, & Reid, 1995). That is to say,
there is little difference between the sensitivity/specificity of the two groups. There are, however, differences that are not intrinsic properties of the data itself, but which are at least as important as considerations of accuracy. Specifically, human-based prediction or benchmarking is not open to inspection in the same way as machinebased prediction or benchmarking. It is possible to retrace the steps taken in the derivation of a piece of information in the case of machines, whereas in the case of humans, the process is hardly open to inspection by the clinician himself, let alone an outside observer. This is an important difference, since it means that one cannot properly identify the causal properties of various recurring patterns of resource-consumption, which is important in improving treatment. Moreover, the process of prediction or benchmarking cannot be systematically modified in the case of humans, since the process itself is largely unknown.
4.3. Processing standardisation Another difference closely associated with the lack of processing transparency in human prediction and benchmarking is that of standardisation. Standardisation is defined here as being when different implementations of the same process (or the same implementation of the same process at different times) generates the same output given the same input. Unlike human-based prediction or benchmarking, machine-based prediction can be standardised. Moreover, such standardisation will be for both between-subjects and within-subjects. Therefore, for example, in the case of within-subject standardisation, for two different patients presenting with the same predictive characteristics, the predictions will be guaranteed to be the same for the machine case, whereas this may not be so for the human case. And in the between-subject scenario, two different machines can be guaranteed to provide the same predictions for the same patient presenting with the same predictive
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characteristics, whereas this may not be so for the human case. Such standardisation is important, since it allows for meaningful comparisons to be made, as well as making the systematic collection and analysis of prediction and benchmarking data and the consequent development of clinical guidelines and critical pathways a meaningful endeavor (Heffner, 1998).
5. General discussion In comparing the non-computerised and the computerised versions of the two models presented above, it is plausible to argue that the computerised elements do not implement de novo system functionality. Rather, they implement pre-existing functionality in different, i.e., computer-based, technology. Naturally, the way in which the new technology generates the informational outputs will be different at a micro-scale, and in that sense some degree of process re-engineering may be said to have been proposed. At a macro level, however, no new processes have been introduced and none removed. Thus, any improvements in system performanceF by whatever criteria such improvements are to be judgedFmust be derived solely from differences in the implementation technology, rather than differences in functionality. The nature of the computerisation proposed in each model involves issues that have previously received little discussion, neither as it impacts on the quality or the cost-effectiveness of healthcare delivery. Yet, the further computerisation of healthcare delivery and the advantages that it brings must be a major factor in achieving both the improved clinical outcomes and productivity goals expected of modern healthcare. In terms of improving clinical outcomes, the proposed computerisation of patient management provides the informational infrastructure necessary for the successful implementation of modern paradigms in patient management, but also for the adoption of evidence-based medicine in the full sense of that term. More generally, it allows for the transformation of healthcare organisations into learning organisations. An assumption of all these philosophies is that improvements in outcomes is based upon a process of the systematic, inspectable and standardised collection, analysis and representation of patient data, all of which can in practice be achieved only through computerisation. Similarly, for the achievement of productivity goals, managers need to exert control over the allocation of resources to patients in such a way that those goals are reached. To do so assumes not only that the manager is in receipt of frequently updated predictions regarding patients’ resourcing requirements, but that these predic-
tions are standardised in terms of both their calibrations as well as their derivations. Moreover, that, as with patient management, the processes which give rise to those predictions are inspectable and systematically programmable, both of which are necessary preconditions for their systematic improvement. The further computerisation for both the scheduling function and the treatment function extends the use of computers into clinical prediction and progress benchmarking, both of which are areas which have traditionally been undertaken by humans. However, the advantages of computerising these processes are just as great, if not greater, than those to be gained from the more basic functions of data collection and storage. Thus, it can be argued that the development of computerised commercial implementations of these processes should be the next logical step in the computerisation of healthcare delivery.
6. Conclusions The main aims of this paper have been to outline the requirement model for two functions of a clinical information system and to examine issues concerning the computerisation of the component functions of those functions. It was argued that there are various advantages to computerisation which go beyond the usual considerations of data storage and retrieval and processing capacity and speed. It was further argued that only a computerised implementation could provide the informational infrastructure necessary to adopt modern paradigms in patient management and achieve productivity targets. A computer implementation of the admission scheduling function is currently being developed as a collaborative project between City University, London and Royal Brompton and Harefield NHS Trust, London. The system will be developed using a Petri net simulation model in combination with a set of clinical prediction models. Various candidate types of model will be evaluated in the development of the clinical prediction models. These will include models based on genetic or evolutionary techniques, linear regression and neural networks. It is hoped that such a resource management tool will encourage a culture of organisational learning and an awareness of issues in the effective management of information and resources at all levels of the organisation, and that this will impact on improving costeffectiveness as much as the more direct mechanism of the system providing informational support. To achieve this, the system will be implemented in stages, with the aim of the first stage to implement only ‘passive’ functionality such as the effective distribution and collection of information. The next stage will involve
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the introduction of some analytical capabilities in the form of decision-support component for the prediction of resourcing requirements. As more and more information is collected, it will then become possible to implement automatic updating procedures for the decision-support components. That is, to update the prediction algorithms used in the prediction of resourcing requirements as the patient population or the effectiveness of available treatment changes. The evaluation of the final system will be based on a comparative study of the performance of the healthcare system before and after implementation. This comparison will be based on both clinical and economic performance variables. The main economic variables will be, for each healthcare unit, the extent to which it increases the rate of resource utilisation and/or decrease the variance in rates of resource utilisation. The main clinical variables will be, for each healthcare unit, the number of cancelled or delayed admissions to that unit, and the total amount of time spend queuing for admission to that unit.
References Barie, P. S., Hydo, L. J., & Fischer, E. (1996). Utility of illness severity scoring for prediction of prolonged surgical critical care. Journal of Trauma, 40, 513–519.
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Buchman, T. G., Kubos, K. L., & Seidler, A. J. (1994). A comparison of statitistical and connectionist models for the prediction of chronicity in a surgical intensive care unit. Critical Care Medicine, 22, 750–762. Davis, D. A., & Taylor-Vaisey, A. (1997). Translating guidelines into practice. Canadian Medical Association Journal, 157, 408–416. Heffner, J. E. (1998). Does Evidence-Based Medicine Help the Development of Clinical Practice Guidelines? Chest, 113, 172S–178S. Marshall, J. C., Sibbald, W. J., & Cock, D. A. (1995). The multiple organ dysfunction (MOD) score: A reliable indicator of a complex clinical outcome. Critical Care Medicine, 23, 1638–1652. Mounsey, J. P., Griffith, M. J., Heaviside, D. W., Hedley Brown, A., & Reid, D. S. (1995). Determinants of the length of stay in intensive care and in hospital after coronary artery surgery. British Heart Journal, 73, 92–98. Seneff, M. G., Zimmerman, J. E., & Knaus, W. A. (1996). Predicting the duration of mechanical ventilation: The importance of disease and patient characteristics. Chest, 110, 469–479. Tu, J. V., & Mazer, C. D. (1996). Can clinicians predict ICU length of stay following cardiac surgery? Canadian Journal of Anaesthesia, 43, 789–794. Watts, C. M., & Knaus, W. A. (1994). The case for using objective scoring systems to predict intensive care unit outcome. Critical Care Clinician, 10, 73–89. Woolf, S. H. (1992). Practice guidelines, a new reality in medicine. Archives of Internal Medicine, 152, 946–952. Zimmerman, J. E., Wagner, D. P., Draper, E. A., & Knaus, W. A. (1994). Improving intensive care unit discharge decicions: Supplementing physician judgement with predictions of next day risk for life support. Critical Care Medicine, 22, 1373–1383.
Towards the development of computerised healthcare delivery in the hospital setting M.A. Hughesa,*, E.R. Carsona, M.A. Makhlouf b,a, C.J. Morganc,a, R. Summersd,a a
Centre for Management and Information in Medicine, City University, London, UK b MITRE Corporation, Bedford, Massachusetts, USA c Department of Anaesthesia, Royal Brompton and Harefield NHS Trust, London, UK d Department of Information Science, Loughborough University, Loughborough, UK Received 4 September 2000; accepted 14 February 2001
Abstract This paper discusses issues relating to the development and evaluation of the next generation of computerised control systems in healthcare delivery. It will be argued that further increases in the cost-effectiveness of healthcare delivery and the successful implementation of organisational learning models of healthcare can best be achieved if computer systems are developed which go beyond the data management functions that typify current systems. It is further argued that the evaluation parameters of such systems are more fundamental and wide reaching than those normally employed. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Biomedical control; Control system analysis; Evaluation; Information systems; Requirements analysis
1. Introduction The clinical information systems considered in this paper are those used to make healthcare resource allocation decisions, i.e. decisions as to which patients should receive which resource inputs (therapeutic interventions), and when. As such a clinical information system can be implemented using a combination of human and computer-based functionality. Historically, however, the computerised element of clinical information systems has consisted of the simple data management functions of data collection, storage and dissemination, with the more complex analysis and decision-making functions, being undertaken by the human componentFthe userFof the system. The purpose of this paper is to model two very general healthcare processes and examine the opportunities they provide for making further advances in the computerisation of healthcare delivery and the advantages accruing thereof. These two processes are both types of healthcare resource allocation process as they occur in a hospital setting, although the conclusions may be applied to any other healthcare setting where those *Corresponding author. E-mail address: [email protected] (M.A. Hughes).
processes occur. The first is that of determining patients’ general resourcing requirements. That is, determining which units within a hospital the patient will be admitted to, and when. The second is determining the patients’ specific resourcing requirements in terms of the therapeutic interventions that are necessary once admitted to a unit. It will be argued that, for both types of resource allocation process, the adoption of various modern patient management paradigms and the fulfillment of resource utilisation targets can only be realistically achieved through the development of systems whose computerised functionality goes beyond that of the simple data management functions that typify current computerised information systems. It will be further argued that many of the advantages distinct to computerised implementations of organisational processes are more fundamental and wide-reaching than those parameters which are normally employed in making comparisons between human-based and machine-based implementations of the same organisational process. The approach that will be adopted is to develop in outline the implementation-independent operational model of an information system for each resource allocation process, referred to hereafter as the schedul-
0967-0661/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 7 - 0 6 6 1 ( 0 1 ) 0 0 1 3 2 - 0
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ing function and the treatment function. Each function will be modelled separately and evaluated in terms of its ability to satisfy the identified requirements. The requirements are discussed in broad qualitative terms from within a control-theoretic and object-oriented framework. Implementation issues will be considered in terms of whether the component informational processes of each function are best implemented in a computer- or human-based system. The structure of this paper is as follows. In Sections 2 and 3, the two outline operational models are presented. In Section 2, the scheduling function model is presented; in Section 3, the treatment function model. In Section 4, issues involving the implementation of the component processes of each function are discussed. In this discussion, attention will be focussed on the question of what advantages there are in computerisation over the existing human-based implementation. In Section 5, a general discussion of the issues raised in the preceding sections is given. This discussion will develop further the themes of Section 4 in an attempt to gauge the impact of greater computerisation on the delivery of healthcare. Finally, in Section 6, the conclusions are summarised and consideration given to the development of the next generation of clinical information systems. This is achieved through a brief description of a system, currently being developed at the Royal Brompton and Harefield NHS Trust, London that implements some of the functionality that will be discussed here.
2. Scheduling function The scheduling function is the process of determining patients’ general resourcing requirements in terms of which hospital units the patient will be admitted to and when. This process usually involves three distinct steps: 1. Determining which resources are or will be available and at what times in which component units of the healthcare facility; 2. Determining which patients require admission and at what times to which component units of the healthcare facility; and 3. Allocating available resources to patients queuing for admission in such a way as to optimise costeffectiveness. The term ‘available resources’ refers to a set of resources that are conjointly sufficient to provide an appropriate level of therapy to an admitted patient. These resources will vary depending on the type of patient admitted, although with most healthcare units admitting only patients with a certain range of diagnoses or levels of severity, the resource inputs required may be considered as a generic package per unit of time, and shall be referred to here as a bed-slot.
In the case of a stand-alone healthcare unit where there is no staged delivery of treatment involving the transfer of patients to or from other healthcare units, assigning patients to available bed-slots is a relatively simple process. However, it becomes much more difficult in the case of progressive-care systems, where the delivery of healthcare is staged according to functionally specialised and interdependent healthcare units. For example, a patient may first be admitted to an operating room, and then subsequently admitted to another unit, such as a post-operative recovery room, and then maybe to another unit such as an intensive-care unit, and so on until the patient is finally discharged. This interdependence between the different units of a progressive-care system means that a scheduler must allocate bed-slots to a patient in several different units simultaneously. Moreover, this has to be accomplished in such a way as to coincide with the patient’s anticipated movement through the system, and the patient’s anticipated length of stay in each unit within the system, both of which may change according to deviations from the individual patient’s expected course of recovery. Put simply, the scheduler must be able to predict the consumption of bed-slots before the consumption can be scheduled. To be able to optimise the scheduling process, two pieces of information are needed: the number of bedslots available in each unit within the scheduling period, and the number of bed-slots in each unit over that period that are required. In the case of a progressivecare system this can be derived from the following variables (for stand-alone facilities, the variables 1b and 2b do not apply): 1.
2.
3.
For patients queuing for admission to the system: (a) Degree of urgency (b) To which units they require admission, and in what order (c) The length of time they will stay in each unit For patients already admitted to the system: (a) Their current location (b) To which subsequent units they will require admission, and in what order (c) The length of time they will stay in each unit, including the current one For each unit: (a) The total number of bed-slots for each unit of time within the scheduling period.
From the above list, items 1a–c may be used to derive the demand for available bed-slots in each unit over time, and items 2a–c and 3a may be used to derive the supply of available bed-slots in each unit over time. The processing involved in the scheduling function as it occurs in a surgical facility is shown in Fig. 1 below. In this diagram, an object-oriented approach is assumed, with actors being represented by clear boxes and the
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Surgeon CreateNew (patient)
Scheduler Schedule (patient)
Clinician UpdateOld (patient)
UpdateOld (bed-slot)
CreateNew (bed-slot)
AllocateNew (bed-slot)
Scheduler AllocateNew (patient)
Fig. 1. Activity diagram of the scheduling function before computerisation.
activities which they perform being represented by shaded boxes labelled by the name of the activity and the object class of which it is a component. Reading the diagram from top to bottom, the actor Surgeon begins by creating a new patient (‘CreateNew(patient)’) in so doing predicting the information items 1a–c from the above list. After a new patient has been created, the actor Scheduler begins the scheduling process (‘Schedule(patient)’) which amounts to placing the new patient onto a list for admission to the healthcare facility. The Actor Clinician then updates the information items 2a–c from the above list (‘UpdateOld(patient)’) and consequently updates the corresponding data on the resources which have already been allocated (‘UpdateOld(bed-slot)’). Scheduler then determines which resources are available to be allocated to patients queuing for admission (‘CreateNew(bed-slot)’), after which available resources may be allocated to queuing patients (‘AllocateNew(bed-slot)’) and viceversa (‘AllocateNew(patient)’). Of the activities involved in the scheduling function shown above, it is proposed here that the computerisation of the activities UpdateOld(patient) and UpdateOld(bed-slot) is possible through the introduction of a new actor called Expert System as shown in Fig. 2. The extent to which the variables 1b,c and 2b,c may be predicted by a computerised expert system on the basis of an evaluation of the patient’s clinical and demographic characteristics remains an open question (see, e.g., Tu & Mazer, 1996). However, many clinical prediction algorithms which have been developed have shown a level of accuracy which approaches or surpasses that of a human predictor (see, e.g. Barie, Hydo, & Fischer, 1996; Buchman, Kubos, & Seidler, 1994; Watts & Knaus, 1994). There is also evidence that
it is possible to predict with a high-degree of accuracy the likelihood of unit discharge over the succeeding 24 h, which suggests that a system of updating should be in place to reflect the dynamics of the patient’s physiology (Marshall, Sibbald, & Cock, 1995). Moreover, it will be argued below that the accuracy of the prediction is only one parameter, which needs to be considered when evaluating different implementations of the same process.
3. Treatment function There has recently been much discussion around various paradigms in patient management such as evidence-based medicine, clinical guidelines, the use of critical pathways, and so on, and in particular how they may be best implemented in the clinical setting (Davis & Taylor-Vaisey, 1997; Woolf, 1992). It is argued here that what is common to all of these paradigms is the need for an informational infrastructure to support their implementation. More specifically, these paradigms are looking upon the process of patient management from a control theoretic perspective and there needs to be developed clinical information systems that are able to support this. Adopting the control-theoretic perspective, such an information system needs three pieces of information for each controlled clinical variable: 1. The present value of the variable; 2. The ideal value of the variable, and; 3. The treatment needed to make the present value the ideal value. The procedural aspects of patient management are shown in Fig. 3. In this figure, the actor Clinician
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Surgeon CreateNew ( patient)
Scheduler Schedule ( patient)
Expert System
UpdateOld ( patient)
UpdateOld (bed-slot)
CreateNew (bed-slot)
AllocateNew (bed-slot)
Scheduler AllocateNew ( patient)
Fig. 2. Activity diagram of the scheduling function after computerisation.
Clinician UpdateOld ( patient)
Benchmark ( patient)
Treat ( patient)
Fig. 3. Activity diagram of the treatment function before computerisation.
starts updating the values for each controlled variable for the patient (‘UpdateOld(patient)’). Then the clinician calculates what the value for each controlled variable should be (‘Benchmark(patient)’). That is, for each variable, calculate what the ideal value would be for the patient, given the patient’s age, operative category, diagnosis, and so on. With this information the clinician can then treat the patient with the goal of reducing the difference between the present value and the benchmarked value for each controlled variable (‘Treat(patient)’). Of the activities involved in the treatment function shown above, it is proposed here that the computerisation of the benchmarking activity Benchmark(patient) is possible through the introduction of a new actor called Benchmarking System as shown in Fig. 4. The extent to which the ideal value of a variable can be algorithmically predicted given a patient’s clinical and demographic characteristics is, as with the prediction of length of stay and admissions requirements above, an open question. However, some recent studies (for example, Zimmerman, Wagner, Draper, & Knaus, 1994; Seneff, Zimmerman, & Knaus, 1996) have shown that such predictions can be made for at least some important variables, and that the making of such predictions can have a beneficial impact on patient management. Again, however, as with the introduction of the expert system in the scheduling function, it will be argued below that the accuracy of these predictions is only one amongst many evaluation parameters.
4. Implementation When one thinks of the basis for comparing computerised clinical prediction with human-based prediction, the immediate reaction is to compare them on the basis of the accuracy of the predictions that they generate. This is an understandable reaction, and if the comparison were made ceteris paribus, it would be a reasonable one. However, when a computer system is implemented all other things do not remain unchanged since new possibilities for enhancing the level of control over the disease process are introduced which were either not previously possible with a human-based system of prediction, or simply not cost-effective. The purpose of this section is to identify those parameters which may change consequent to the proposed computerisation of the scheduling and treatment functions described above. In particular, three parameters are identified: processing capacity, processing transparency and processing standardisation. These parameters will be discussed in turn. 4.1. Processing capacity With regard to frequency of measurement of the variables, items 2a, 3a of the scheduling function, and items 2 and 3 of the treatment function need be measured relatively infrequently. The other variables, however, are constantly changing because the patient’s underlying physiology from which all those variables are
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Expert System
UpdateOld ( patient)
Benchmarking System
Expert System
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Benchmark ( patient)
Treat (patient)
Fig. 4. Activity diagram of the treatment function after computerisation.
at least partly derived is constantly changing. Thus, the frequency of updating of this information determines the extent to which the system can be controlled. The more frequently a variable is measured, the more control the scheduler can exert over the progressive-care system or the clinician can exert over a patient’s therapy, and hence the more likely that performance will be optimised in each case. This requirement places a great burden on an implemented information system in terms of data requirements that only a computerised implementation can realistically accomplish. Consider the quantity of information involved. For example, the prediction of length of stay (items 1c and 2c of the scheduling function) is made on the basis of various physiological, therapeutic, and demographic parameters such as age, blood pressure, operative category, and so forth. If one makes a conservative estimate of there being 10 data entries for each prediction, 5 of which correspond to dynamic underlying physiological variables. If one further assumes that predictions need to be updated every hour, for a total of 50 patients, then it soon becomes impractical, or at least not cost-effective, for a human-based data collection system to be implemented. Likewise for the measurement of physiological variables in the treatment function, where updating may need to be even more frequent, every minute or every second. Thus, insofar that a computerised system is able to cost-effectively provide a greater proportion of the information which is needed than a human-based system, one is able to say that computerisation offers greater scope for control. 4.2. Processing transparency In the case of the prediction of clinical variables, there is no reason to assume that machine prediction is any better or worse than human (i.e., clinician) prediction or benchmarking (see, for example, Mounsey, Griffith, Heaviside, Hedley Brown, & Reid, 1995). That is to say,
there is little difference between the sensitivity/specificity of the two groups. There are, however, differences that are not intrinsic properties of the data itself, but which are at least as important as considerations of accuracy. Specifically, human-based prediction or benchmarking is not open to inspection in the same way as machinebased prediction or benchmarking. It is possible to retrace the steps taken in the derivation of a piece of information in the case of machines, whereas in the case of humans, the process is hardly open to inspection by the clinician himself, let alone an outside observer. This is an important difference, since it means that one cannot properly identify the causal properties of various recurring patterns of resource-consumption, which is important in improving treatment. Moreover, the process of prediction or benchmarking cannot be systematically modified in the case of humans, since the process itself is largely unknown.
4.3. Processing standardisation Another difference closely associated with the lack of processing transparency in human prediction and benchmarking is that of standardisation. Standardisation is defined here as being when different implementations of the same process (or the same implementation of the same process at different times) generates the same output given the same input. Unlike human-based prediction or benchmarking, machine-based prediction can be standardised. Moreover, such standardisation will be for both between-subjects and within-subjects. Therefore, for example, in the case of within-subject standardisation, for two different patients presenting with the same predictive characteristics, the predictions will be guaranteed to be the same for the machine case, whereas this may not be so for the human case. And in the between-subject scenario, two different machines can be guaranteed to provide the same predictions for the same patient presenting with the same predictive
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characteristics, whereas this may not be so for the human case. Such standardisation is important, since it allows for meaningful comparisons to be made, as well as making the systematic collection and analysis of prediction and benchmarking data and the consequent development of clinical guidelines and critical pathways a meaningful endeavor (Heffner, 1998).
5. General discussion In comparing the non-computerised and the computerised versions of the two models presented above, it is plausible to argue that the computerised elements do not implement de novo system functionality. Rather, they implement pre-existing functionality in different, i.e., computer-based, technology. Naturally, the way in which the new technology generates the informational outputs will be different at a micro-scale, and in that sense some degree of process re-engineering may be said to have been proposed. At a macro level, however, no new processes have been introduced and none removed. Thus, any improvements in system performanceF by whatever criteria such improvements are to be judgedFmust be derived solely from differences in the implementation technology, rather than differences in functionality. The nature of the computerisation proposed in each model involves issues that have previously received little discussion, neither as it impacts on the quality or the cost-effectiveness of healthcare delivery. Yet, the further computerisation of healthcare delivery and the advantages that it brings must be a major factor in achieving both the improved clinical outcomes and productivity goals expected of modern healthcare. In terms of improving clinical outcomes, the proposed computerisation of patient management provides the informational infrastructure necessary for the successful implementation of modern paradigms in patient management, but also for the adoption of evidence-based medicine in the full sense of that term. More generally, it allows for the transformation of healthcare organisations into learning organisations. An assumption of all these philosophies is that improvements in outcomes is based upon a process of the systematic, inspectable and standardised collection, analysis and representation of patient data, all of which can in practice be achieved only through computerisation. Similarly, for the achievement of productivity goals, managers need to exert control over the allocation of resources to patients in such a way that those goals are reached. To do so assumes not only that the manager is in receipt of frequently updated predictions regarding patients’ resourcing requirements, but that these predic-
tions are standardised in terms of both their calibrations as well as their derivations. Moreover, that, as with patient management, the processes which give rise to those predictions are inspectable and systematically programmable, both of which are necessary preconditions for their systematic improvement. The further computerisation for both the scheduling function and the treatment function extends the use of computers into clinical prediction and progress benchmarking, both of which are areas which have traditionally been undertaken by humans. However, the advantages of computerising these processes are just as great, if not greater, than those to be gained from the more basic functions of data collection and storage. Thus, it can be argued that the development of computerised commercial implementations of these processes should be the next logical step in the computerisation of healthcare delivery.
6. Conclusions The main aims of this paper have been to outline the requirement model for two functions of a clinical information system and to examine issues concerning the computerisation of the component functions of those functions. It was argued that there are various advantages to computerisation which go beyond the usual considerations of data storage and retrieval and processing capacity and speed. It was further argued that only a computerised implementation could provide the informational infrastructure necessary to adopt modern paradigms in patient management and achieve productivity targets. A computer implementation of the admission scheduling function is currently being developed as a collaborative project between City University, London and Royal Brompton and Harefield NHS Trust, London. The system will be developed using a Petri net simulation model in combination with a set of clinical prediction models. Various candidate types of model will be evaluated in the development of the clinical prediction models. These will include models based on genetic or evolutionary techniques, linear regression and neural networks. It is hoped that such a resource management tool will encourage a culture of organisational learning and an awareness of issues in the effective management of information and resources at all levels of the organisation, and that this will impact on improving costeffectiveness as much as the more direct mechanism of the system providing informational support. To achieve this, the system will be implemented in stages, with the aim of the first stage to implement only ‘passive’ functionality such as the effective distribution and collection of information. The next stage will involve
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the introduction of some analytical capabilities in the form of decision-support component for the prediction of resourcing requirements. As more and more information is collected, it will then become possible to implement automatic updating procedures for the decision-support components. That is, to update the prediction algorithms used in the prediction of resourcing requirements as the patient population or the effectiveness of available treatment changes. The evaluation of the final system will be based on a comparative study of the performance of the healthcare system before and after implementation. This comparison will be based on both clinical and economic performance variables. The main economic variables will be, for each healthcare unit, the extent to which it increases the rate of resource utilisation and/or decrease the variance in rates of resource utilisation. The main clinical variables will be, for each healthcare unit, the number of cancelled or delayed admissions to that unit, and the total amount of time spend queuing for admission to that unit.
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