Reprinted with the kindpermission of Editrice Kurtis, publishers of Diabetes, Nutrition &Metabolism: Clinical andExperimental

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Application of computers inclinical diabetes care

Not much has changed in 75 years. At around thesame time as Banting and Best were reporting their preliminaryfindings (3) the field of engineering was beginning to lay thegroundwork of control systems theory. These seemingly unrelatedendeavours were brought together when medical researchersrealised that metabolic controls in the body could be describedand analysed using the theories and techniques developed forfeedback control systems in engineering (4).

Medical Informatics, the officialjournal of the European Federation for Medical Informatics,recently devoted a two-part Special Issue to advanced informationtechnology (IT) initiatives that may be able to address theproblem of adjusting the insulin dose, and controlling bloodglucose (BG) levels, as well as assisting in the provision ofmodern-day diabetes care (5, 6). This review focuses onsome of the more clinically relevant papers included in theSpecial Issues (5,6), which follow previous journalissues and conference proceedings on the subject of computers indiabetes.

However, the Special Issues do not consist ofpresentations arising from a particular meeting, but rather aremade up of papers specifically invited from recognised experts inthe field. Other novel work - as yet unpublished in the medicalcomputing / diabetes literature - was also sought for inclusion;the aim being to produce two volumes describing new and excitingwork in this important area. The niche for such Special Issuesarose from a systematic two-part review by Lehmann and Deutsch (7,8) of the application of computers in diabetes care,which appeared in Medical Informatics in late 1995.While researching this it became apparent that there was somevery exciting and novel work which had not found its way into theliterature. In certain cases new but as yet unpublished work wasbeing done by recognised experts in the field. However, in othercases highly novel and pertinent research was being carried outby non-academic workers, often outside the academic centres ofexcellence usually associated with such ventures.

The papers selected for inclusion in theSpecial Issues can broadly be classified under the followingheadings: (i) databases, (ii) algorithms, (iii) decision support,(iv) models, and (v) education; each of these five topics beingaddressed by a trilogy of papers. The more clinically relevant ofthese articles will be discussed here. First, however, a briefoverview of the computing background is provided and the clinicalneed for and potential cost-benefits of utilising IT in diabetescare are considered.

COMPUTING BACKGROUND

Endeavours to apply IT routinely in diabetescare have been attempted for many years. Significant advanceshave been made by a number of key researchers. However, despitethe novel work undertaken it is fair to say that the impact ofcomputers on diabetes care thus far has been relatively limited. Fig. 1 summarises the spectrum of IT applications relevant toclinical diabetes care. The acceptance scale indicates how widelysuch applications have been adopted into routine clinicalpractice. For example, BG meters (either with or withoutelectronic memories) are well accepted tools. By contrast at theother end of the spectrum computerised decision support is not atall widely used, at present (9).

Clearly a number of earlier attempts at the‘intelligent’ application of computers in diabetes carewere not sophisticated enough. In many cases user interfaces wereprimitive and prototypes difficult to use. However suchdevelopments were not helped by the fact that many physicianswith busy clinical practices did not see a need for, or have adesire to use, such software programs. Also system developerscertainly can be faulted for not demonstrating through rigorousretrospective and prospective clinical trials the safety andefficacy of their applications.

THE NEED FOR IT IN DIABETES CARE

Things however appear to be changing. Theimpact of the Diabetes Control and Complications Trial (DCCT) (10) cannot be overemphasised. It is now apparent thatexisting standards of diabetes care are not sufficient.Tightening glycaemic control really makes a difference to patientwellbeing - yet the facilities are not available to provide thesort of intensive insulin therapy applied in the DCCT in routineclinical practice. The resources needed to achieve and maintainreally tight diabetes control in the DCCT were enormous.Specialist staff were recruited to maintain contact with patientswho were often seen fortnightly in hospital, and contacted evenmore frequently by telephone during the day as well as at night.Efforts were redoubled at times of minor illness or emotionalupset. Team meetings took place weekly to maintain thesestandards. Excluding research costs, the annual clinical cost ofintensive therapy in the DCCT was US$ 4000 per year for multipledaily injections and $ 5800 per year for continuous subcutaneousinsulin infusions - approximately three times the cost ofconventional therapy ($ 1700 per year). A large portion of thiscost difference was reported to be related to the greaterfrequency of out-patient visits and the greater resources used inself-care (11). Clearly on apopulation-wide basis it will be hard to sustain three-timesgreater expenditure for diabetes care in routine clinicalpractice.

In a recent survey, 96% of outpatient visitsfor primary care of patients with diabetes were made to generaland family practitioners and internists. Such health-careprofessionals did not provide the care in the DCCT. Also the vastmajority of primary care physicians lack the training andresources to offer DCCT-like intensity of care (12).Furthermore for the bulk of primary care physicians who have onlya few patients needing DCCT-like care, it is simply not costeffective to provide the necessary staff or allocate the timerequired to deliver this intensity of service (13).

The findings of the DCCT come at a time whenmajor health reforms are underway in an attempt to reduce thespiralling costs of health-care provision in the USA, as well asin other countries. In 1992 the direct costs of all forms ofdiabetes mellitus and its complications in the USA were estimatedat $45 billion (14), while in 1996 in the UK the cost of non-insulindependent (type 2) diabetes mellitus was estimated at £2billion, or 8% of National Health Service hospital expenditure (15). Therefore the time may be right for the utilisationof IT in diabetes care. Indeed it is difficult to envisage thewidespread delivery of improved glycaemic control, such as thatseen in the DCCT, without the extensive use of IT. In thisrespect it is not a case of replacing doctors, nurses or otherhealth-care professionals - rather there is a need for the volumeof work undertaken to increase - yet the staff required to dothis simply are not available. While the capital costs fordeveloping, validating and utilising computer systems to helpwith this clinical workload may be high - the savings which couldaccrue are substantial (16,17). So in purely financialterms the case for IT in diabetes care may be compelling - ifsystems can be developed which really work and generate clinicalbenefit.

DATABASE SYSTEMS

Initiatives to reduce the suffering associatedwith diabetes mellitus include the St. Vincent Declaration (18) which is in part looking to establish monitoring andcontrol systems using IT for quality assurance of diabeteshealth-care provision and for laboratory and technical proceduresin diabetes diagnosis, treatment and self-management. Now sevenyears on a multitude of database applications are available tomonitor and document the level of care provided to patients withdiabetes (19,20). While it could be argued that too many differentdatabase programs are being developed - and therefore perhapsefforts are unnecessarily being duplicated - hopefully at leastcommon datasets are starting to be being agreed upon (21) so that regardless of the application being used -data will eventually become compatible and therefore accessiblelocally, regionally and nationally for research and audit.However, much work needs to be done to ensure data security andsafeguard patient confidentiality - especially as data start tobe transferred between primary and secondary care and betweenhospitals and regions.

While databases may not be viewed as the mostexciting area of medical informatics research - they are an areawhere a clinical impact is being routinely made, now. Alsoperhaps one of the failings of earliersemi-‘intelligent’ decision support prototypes restswith the lack of stable, computerised medical record systemswhich could ‘feed’ the decision-support software withdata. The need for decision support programmers to design andcode their own individual small databases unnecessarily prolongedthe development cycle, increased compatibility problems, and madethe wider dissemination of the prototype systems for evaluationby others all the more difficult.

In part 1 of the Special Issue Kopelman andSanderson (22) provide an introductory overview of some of therequirements of database systems in diabetes care - beforedemonstrating how their own application, DIAMOND,addresses these. Particular attention is devoted to the St.Vincent Declaration and the ways in which databases can assist inthe audit required for this, as well as to help ascertainclinical outcome measures. The following paper by Engelbrecht etal (23) overviews a particular database application - adiabetes smart card - highlighting some of the requirementsspecific to this form of patient-held database. Results of apreliminary evaluation of the DIABCARD in a hospitalsetting are also presented. Interestingly acceptance of this newtechnology by patients appears to be greater than that by medicalstaff.

Efficient data analysis and decision makingboth depend heavily on the frequency and quality of communicationbetween patients and health care professionals. The trilogy ofdatabase-related papers is concluded with an overview by Gomez etal (24) of their DIABTel system. This focuses on analternative distributed database architecture - offering atelemedicine based approach for diabetes care (Fig. 2) which aims to: [i] improve patient communication withhospital-based diabetologists between clinic visits, [ii] allowdoctors to assess the patient’s condition on a more frequentbasis (e.g. every week), and [iii] provide patients with aservice of ‘supervised autonomy’ to increaseindependence without decreasing the necessary continual supportand supervision of the doctor. Clinical evaluation studies ofthis approach are planned, and the concept and systemsarchitecture are to be developed further in the new telematicsT-IDDM project (25). Separate telemedicine experience from other centressuggests the DIABTel objectives to be realistic.Fallucca et al (26) recently reportedelsewhere on the application of the Diva system (27) in a cohort of pregnant diabetic women. Use of thetelemedicine software led to tighter BG control, in particularfaster optimisation of BG profiles, better pre-breakfast andpre-lunch BG concentrations and fewer hypoglycaemic episodes (27) - results which if confirmed in large-scale evaluationstudies would be most encouraging.

ALGORITHMIC-BASED DECISION SUPPORT SYSTEMS

This author started his research interest inthe application of computers in diabetes care in an academicDepartment of Medicine in a University teaching hospital wherealgorithms were frowned upon, because they could not explaintheir reasoning. This is a valid criticism, and certainly alimitation of algorithmic-based approaches. As a result theauthor embarked on the development of a series of prototypeslinking knowledge-based systems (KBS) with linear models, andcompartmental models, utilising various means of datainterpretation and feature extraction. A retrospective on what isnow a 7+ year voyage has recently appeared elsewhere (28). None of the KBS or model-based approaches weresufficiently robust or safe to be used as insulin-dosage advisorsand having reviewed the literature (7,8) and considered ‘why not’ it has becomeapparent that algorithmic-based approaches do offer acomputational robustness and practicality difficult to provide,at present, by other means.

Things obviously however do rather depend onthe purpose of seeking computer-based assistance. If usersrequire to analyse why a patient may be going hypoglycaemicovernight - or what the contribution of the glucosecounter-regulatory process (Somogyi effect) might be - clearlyalgorithms may not be of great benefit. However most doctors donot look to IT to help them in this way. It is rare for acompetent diabetologist or endocrinologist not to know how toimprove a given patient’s glycaemic control. This obviouslymay not be the case for general primary care physicians - but tomanage patients with diabetes non-specialists are unlikely torequire such complex (physiological) analyses either.

The basic problem appears to arise becauseclinicians have insufficient time to see patients frequentlyenough - from the control systems perspective the under-sampledsystem with infrequent BG monitoring and even less frequentvisits to a doctor can impact significantly on patients’diabetes care. Also not having enough trained health-careprofessionals (nurses, educators, dieticians, etc) tomotivate patients to make the necessary adjustments themselvescan cause problems.

Algorithms by their very nature cannot copewith situations not explicitly stated. However they can cater fora large variety of situations - and if they can offer 90%coverage this could provide significant practical benefit to aconsiderable number of patients. While intellectually it may notbe very satisfying to utilise algorithms which are notcomprehensive - and while algorithms may be one of the lessintellectually taxing methodologies in diabetes computing - andtherefore perhaps not of such great interest to academics - theydo seem to warrant closer attention.

In part 1 of the Special Issue Albisser etal (29) describe their algorithmic, telemedicine-based HumaLinksystem (formerly called TeleDoc (8)).Having measured their BG levels patients can access the HumaLinksystem from any touch-tone telephone, whether at home or whiletravelling, 24 hours a day. Patients are encouraged to call HumaLinkat each BG measurement, if possible, and particularly when aninsulin dosage is to be recommended. After identifying themselveswith their own unique personal identification number, patientsfollow verbal instructions from the speaking HumaLinksystem and key in their BG measurement(s) along with any otherinformation about illness, hypoglycaemia, changes in activity,unusual carbohydrate intake and medication. The system verballyverifies each entry before accepting it into the patient’spersonal diabetes database.

The HumaLink system then relaysinstructions in accordance with an individualised treatment planprogrammed by the caller’s physician. Following thecomputer’s provision of verbal advice down the telephoneline the system requires patient confirmation of the instructionsHumaLink has delivered by verbally requesting thatpatients key-in the new insulin doses.

HumaLink can operate in‘manual’ or fully automated advisory mode. In‘manual’ recording / documenting mode the computer logsthe patient’s readings and a physician reviews the databefore leaving a verbal message for the patient on the system.The next time the patient telephones HumaLink he / shewill receive the physician’s advice about what therapyadjustment(s) to make. Furthermore, health-care professionalshave the facility to activate a virtual recorder and using amicrophone can leave messages for individual patients regardingspecific instructions. All interactions by the health-careprofessional are documented and become part of the patient’slegal medical record. A facility is also provided to forwardcourtesy reports automatically by fax from the HumaLinkcomputer to the patient’s referring physician (8).

The fully automated advisory mode appliesalgorithms to modify insulin dosages within pre-defined limitsset by the physician. Using trigger levels, guidelines, andinstructions individualised for each patient, the system can alsoautomatically react on behalf of the physician immediately when acrisis (such as hypoglycaemia) is reported or whenever BG controldeviates from the targets set for that particular patient.Details of the advisory module which utilises a pursuit algorithmhave been previously described by Albisser (30).

If the patient’s BG profile does notrespond in the manner expected, this is flagged by the computerfor the physician’s attention. The guidance provided and / orpre-set threshold levels are then reset in order to direct thepursuit algorithm in an alternative, more appropriate direction.These interactive capabilities, together with automatic safetylimits built into the system, are intended to ensure patientsafety. While it is possible to construct rules and algorithms tobe conservative and therefore which should theoreticallybe safe - apprehensions always exist about computers deciding onpatient therapy without human intervention. Clearly this is not aconcern restricted to diabetes computing - but rather a much moregeneral issue - with medico-legal implications (31)for the entire decision-support field of medical informatics (32).

With the HumaLink system such concernsare at least in part addressed by allowing periodic expert humanclinical review of the data and possible human intervention, ifrequired, via the central telemedicine computer. By contrast suchsupervisory input clearly cannot be provided at present withhand-held devices. The US Food and Drug Administration (FDA)Center for Devices and Radiological Health has determined that HumaLinkis a medical device as defined under Section 201(h) of the USFederal Food, Drug and Cosmetic Act (29).

Algorithms cannot explain or justify theirreasoning - either to patients or health-care professionals. Thismay account for the relative lack of widespread use ofAlbisser’s earlier Insulin Dosage Computer (30) which was effectively a ‘black box’ whichclinicians and patients just had to trust. This limitationappears to have in part been addressed in the HumaLink systemwhere the algorithms are generated for individual patients bytheir clinician, from a framework devised by Skyler et al(33). Therefore different algorithms can be used fordifferent patients - although standard ‘templates’ areavailable.

How well does HumaLink manage inclinical practice? In part 1 of the Special Issue preliminary‘b-testing’experience is reported from 124 insulin-treated diabetic patientsin two US centres, compared with 80 insulin-treated diabeticcontrols (29). Further studies in other centres are on-going. Atbaseline, before starting to use HumaLink, thesepatients had HbA_1c levels of 10.1-10.2%. After sixmonths HbA_1c levels had fallen 1.0-1.3% in thosepatients actively using the system, but remained unchanged in thecontrol group who received routine therapy from their localdiabetologist (Fig. 3). Clearly it could bethat patients who actively used the system were better motivatedand more interested in their diabetes, compared with those whodid not make use of HumaLink - although it is noteworthythat there were no significant differences in HbA_1clevels between users and non-users at baseline. However toovercome this as a potential confounder fully randomised trialsare required.

Notwithstanding the non-random nature of therecruitment, it is interesting to note that in this large cohortthere were no reported events of serious hypoglycaemia (requiringassistance or hospitalisation) attributable to the system ineither centre. Furthermore the prevalence of diabetes relatedcrises (hyper- or hypo-glycaemia) was reported to fallapproximately three-fold in the active use group (29).

Albisser and colleagues (29) are the first to report an evaluation of a decisionsupport prototype in such a large number of diabetic patients.Even though their study is only preliminary and a safety andefficacy b-testingeffort - the findings are substantial - and therefore certainaspects of the evaluation approach should be highlighted. Thestudy had wide inclusion criteria - providing a heterogeneouspatient population similar to that which would be found inroutine clinical practice. While this may be more realistic thanjust studying highly selected subgroups of patients, in futurestudies it would be useful for the data or analyses to bestratified by patient type and status, to obtain some insightinto the utility of the system in selected sub-groups. Forexample, the HumaLink approach might be better accepted,and therefore more utilised, by younger patients. However thecurrent evaluation study does not permit such subgroup analyses.

Also, while the overall numbers of patientsstudied were large, and involved we are told in total 888 patientmonths of prospective follow-up, HbA_1c data were onlyavailable at the end of the 6-month follow-up from 90 users and77 non-users. Future reports should clearly aim to follow up alarger proportion of both users and non-users, and report HbA_1cdata on all the patients studied, lest biases be introduced bythose subjects ‘lost’ to follow-up or for whom suchrepeat HbA_1c data were not available. For examplethose patients who did not have such frequent blood tests andtherefore missed their 6-month follow-up may have had worseglycaemic control as a result of being seen less often in clinic.Alternatively these patients may have derived less benefit fromtheir use of the HumaLink system and thereforere-attended clinic less frequently. Whatever the reason - wecannot exclude that the active users for whom follow-up HbA_1cdata are not available represent the sub-group of theintervention cohort whose glycaemic control did not improve. Ifthis is the case - loss of these patients to follow-up couldsignificantly skew the results in favour of a positive outcome.Also the differential loss of patients to follow-up, at least 20%of the intervention group as compared with only 13% of the"control" group, may have introduced further biasesinto the analyses. Such considerations should be borne in mind byresearchers planning evaluation studies with alternativedecision-support prototypes, as well as for future studiesinvolving the HumaLink system.

Furthermore, it is well recognised thatglycaemic control can improve simply as a result of beingenrolled in a study, or potentially in this case from theattention provided by telephoning HumaLink each time aBG measurement is made. Therefore fully randomised studies arevery much needed.

However if improvements in HbA_1clevels, such as those reported by Albisser et al (29), can be confirmed by further long-term, large-scalerandomised clinical trials this will be a most encouragingdevelopment for patients with diabetes. By comparison, in theDCCT (10) the 2% difference in mean HbA_1c between thestandard and intensively treated groups was associated with a 60%reduction in risk for diabetic retinopathy, nephropathy, andneuropathy.

It would also be of considerable clinicalinterest to establish whether the patient benefits of using the HumaLinksystem persisted, even after discontinuing use - i.e. do patientslearn from their telephone interactions with the computer, ordoes it only offer a ‘crutch’ on which they becometotally dependent? As intimated above, medico-legal issues havealways been a concern to developers of decision-support systems.Therefore it is interesting to note that the first law-suitregarding the HumaLink system has already taken place inthe USA. This dealt with the denial by an insurance company of apatient’s claim for the medical care provided by thecomputer system. The ruling was in favour of the insurancecompany and the settlement was that no reimbursement for computeror telephone assistance would be provided. The insurance companydid however offer unlimited, even daily, clinic visits for thepatient - to be reimbursed without contest (Dr. A.M. Albisser,personal communication).

EDUCATIONAL SYSTEMS

"Glucose measurements are futile ifnot acted upon"
Charles Best

Despite the advanced technology which is beingdirected to the measurement and storage of SMBG data manypatients, even in the 1990s, appear poorly equipped to altertheir therapy on the basis of such data. When one considers thatpoor glycaemic control is associated with an increased later liferisk of a plethora of devastating complications it seemssurprising that more effort has not gone into educating diabeticpatients about what to do with their BG readings.

For example, although essential to the process,it has repeatedly been shown that the isolated act of collectingSMBG data is not sufficient to improve metabolic control (34,35). While educational work may be considered less‘glamorous’ than the latest artificial intelligenceendeavours - education is a key means of improving understandingin medicine. As computer-assisted learning (CAL) techniques arerevolutionising the way that people are taught, it is likely thatCAL techniques applied to health-care education may providepowerful tools for the teaching and motivation of both patientsand students. Furthermore with the massive expansion of theInternet over the past few years there at last appears to be astable common platform which will greatly increase accessibilityto on-line teaching from (remote) centres of excellence.

Is diabetes education already not widelyavailable? The simple answer is ‘no’. In a recentsurvey of over 2400 patients to determine the proportion ofadults with diabetes in the USA who had received diabeteseducation, it was found that over 41% of type 1 diabetic patientshad never attended a class or program about diabetes (36). Furthermore even when education is made available,physicians often tend to explain the disease rather than ensurethat patients acquire the reflexes and expertise that willgenuinely enable them to manage their diabetes (37). Therefore there is clearly room for improvement, andperhaps IT can help. However, education is difficult if basedonly on verbal and written presentations of dry facts (38). Teaching materials using multi-media presentationsmay provide a partial solution. However, the aim should also beto teach diabetes self-management to patients in an intuitive andenjoyable way, so that the knowledge can be enduring. Clearly itis not ideal to learn about diabetes control solely from reallife experiences because of the long time frame involved, asidefrom the possible dangers to the patient of hypo- orhyper-glycaemia. An interactive simulation of a diabetic patientcould be one solution.

As Chao (4) has highlighted, in thesame way that aircraft pilots and air traffic controllers aretrained for routine and emergency procedures on airplane and airtraffic simulators, it should be possible for diabetic patientsto be trained for physiologically appropriate responses on adiabetes simulator in absolute safety, and in a relatively shortspace of time. In this respect management of error is a keycomponent of the learning process. Identifying the error, gettingpatients to discover it for themselves, and asking howthey would correct it, are essential steps in educating patientshow to improve their glycaemic control (37).

This point was addressed in part 2 of theSpecial Issue by a paper which overviewed the application ofdiabetes simulators - in particular those based on compartmentalmodels - for use in the education of health-care professionals,students, patients and their relatives (39). The purpose of these tools is to create a learningenvironment for communicating and training intuitive thinkingwhen dealing with insulin dosage, dietary and lifestyleadjustments. As Biermann and Mehnert (40) have highlighted such educational simulators areintended "neither to compete with normal diabeteseducation nor with dose adjustment algorithm programs, but ratherto support these ... The increasing number of young persons withcomputer experience suggests an increasing acceptance of computersimulation and learning programs. This is particularly importantwhere patient management of the disease is demanded".While the issues of supporting intuitive learning together withthose of validating educational interventions were addressed -the relative paucity of evaluation data for such software wasalso highlighted as a severe limitation of such work and aresearch area which needs a great deal more attention and medicalcollaboration (39).

One particular simulator, called AIDA (Fig. 4a), was overviewed in greater detail (39). The software was offered in June 1996 without chargeas a freeware program on the WorldWide Web (41, 42). Thus far over people have visited the Web site where the program is stored - http://www.2aida.org - and to date over copies of AIDA havebeen downloaded gratis (Fig. 4b). This software represents one of an increasing numberof diabetes-related programs which are becoming available, orbeing widely distributed, via the Internet. A version of thisprogram, on diskette, with a printed, bound manual has also justrecently become available to health-care professionals in the UKfrom the British Diabetic Association (London, UK) (43).

Chao (4) has also previouslysuggested that a proper evaluation of educational simulatorscould test patients for proficiency in the management of a givensimulated test subject before and after programmed instruction[for example, similar to that described by Dammacco et al(44)] but with the addition of the simulator. Computerscould evaluate the performance of the user by computing thenumber of hypoglycaemic reactions experienced by the simulatedtest subject, the average and standard deviations of the recordedBG levels, and the average and maximum amount of insulinadministered during treatment; such quantitative analyses beingused to (hopefully) demonstrate improvements in knowledge andability. Comparisons of runs made before and aftersimulator-assisted instruction could also potentially be used toevaluate the effectiveness of this approach to education.Furthermore it has been suggested that the use of computers mightremove some of the subjectiveness from the evaluation process andprovide an impartial method of determining performance (4).

The issue of evaluating educationalinterventions was also addressed in the following paper in part 2of the Special Issue by Brown et al (45)who overviewed Packy & Marlon (Raya Systems Inc.,California, USA), an interactive computer game for the SuperNintendo™ platform, which is designed to improve self-caremotivation and behaviour among children with diabetes. Packy& Marlon is a role-playing game in which players managethe diet and insulin of two elephants who have diabetes (Fig. 5). To optimise the educational benefits of this gameplayers can select insulin plans which match their own. Thescenario is a diabetes summer camp which has been raided by rats.The two elephants, Packy and Marlon, need to defendthemselves by blasting the attacking rodents with peanuts andwater from their trunks. They also need to find food and supplies- remembering to eat healthily, regularly check their BG levels,and take their insulin! Not only does this game provide childrenwith a fun way to learn about diabetes, but it is also intendedto aid dialogue between children with diabetes and theirnon-diabetic friends.

Packy & Marlon was assessed in twoUS centres in a 6-month randomised controlled trial, in a cohortof 59 insulin-dependent (type 1) diabetic children. Half thecohort received the diabetes game to use at home as much as theyliked, while the other half (the control group) received a videogame with no health-care content. While significant improvementsin HbA_1c were not demonstrated the authors quitecorrectly highlighted that the patients were reasonablycontrolled at the start (mean baseline HbA_1c :8.3-8.5%) and therefore quite possibly the study ran into a‘ceiling effect’. Clearly to overcome this problemfurther randomised-controlled trials with diabetic children withmore usual (poorer) glycaemic control would be required.Notwithstanding this, benefits were reported in diabetesself-efficacy, communication with parents about diabetes andself-care behaviour in the children who received Packy &Marlon. Also there was a decrease in unscheduled urgentdoctor visits, a finding which if confirmed by larger studieswould be most encouraging for children with diabetes.

The final paper in the education trilogy by Dayet al (46) overviewed an alternative type of learning tool - amulti-media based educational package for patients, carers andhealth-care professionals. One of the strengths of this systemlies in its breadth of coverage - a whole host of practicalinformation about diabetes and nutrition being provided in aneasy to access interactive manner on CD-ROMs (Fig. 6). As a valuable feature - rather than being‘lectured’ by health-care professionals - patientsthemselves have been filmed on video recounting their ownexperiences with their diabetes and these clippings - 2 ½ hours in total - form one of the mainstays of thesystem. As an example of the sort of information stored, apatient recounting his own experiences of going hypoglycaemic isprovided. Intuitively this may well be more informative and a lotmore interesting for other patients than being told by a doctoror nurse what it may feel like to have a ‘hypo’. Anexample display from Day and colleagues’ LearningDiabetes system is shown in Fig. 6. ‘Maintaining the balance’ is a screen whichallows the user to experiment with a combination of food, drink,insulin injections and exercise to discover the effect of suchinputs on BG levels. Using a ‘drag and drop’ processthey can build up a day with any combination of these inputs thatthey wish, and at the press of a button they can reveal the BGprofile predicted to be produced (46).

DISCUSSION

It is not possible to cover in this review allof the 15 papers included in the SpecialIssues (5,6). Instead attention has been directed to the more clinicallyrelevant articles - especially those focusing on databases,algorithmic-based decision support, education - and the clinicalevaluation of such systems. However, the diversity of approachesreported in the Special Issues (5,6), effectively to solve a single problem - maintainingglycaemic control - is intriguing (47). Some researchers propose dose-by-dose adjustments,while others prefer longer-term visit-by-visit therapeuticinterventions. Some prefer data-driven paradigms while othersdepend on model-based approaches. Some have tried to usequalitative advisors, others algorithms, and yet others causalprobabilistic network (CPN)-based approaches. Some rely onhand-held devices for the patient whereas others make use ofpublic telephone networks. Furthermore as the trend fortreatment, mainly driven by financial forces, is now towardsself-administered management - education becomes an increasinglyimportant component of the care offered to diabetic patients.

Some clinicians who promote educationalinterventions have argued against a ‘crutch’ forpatients - which might increase reliance on a technologicaldevice while decreasing their ability to self-manage. However itshould be apparent that not all patients are the same. While someare motivated and interested and will take the time and troubleto learn more about their diabetes and truly understand how toadjust their insulin doses on the basis of SMBG data, andtherefore could clearly benefit from further education, othersappear to have no such interest and providing them with asolution whether it be in the form of a hand-held device, ortelephone access to a central computer may be what they require.

Clearly there will always be patients whorequire greater freedom. However such increased flexibilitybrings with it a requirement for increased complexity which mayor may not be acceptable or usable by the vast majority ofpatients. For example the HumaLink system (29) offers the possibility of patient flexibility but atthe ‘price’ of requiring frequent telephone calls. Notevery patient will wish to have to telephone a central computerafter each BG measurement or before each insulin injection - butsome clearly could benefit from such support. Looking to thefuture, with the miniaturisation of electronics and the extensionof mobile telephone networks, it is not inconceivable to considera day when a BG meter will incorporate a mobile telephone and beable to automatically dial a central computer, transmitting dataafter each BG measurement. The central computer’s advicecould be transmitted back and shown on the BG meter’sdisplay. By having the possibility of human review andintervention at the central computer - many of the concerns aboutpatients acting solely on the basis of advice from a stand-alonemachine may be mitigated. When there are many ways of doingsomething in medicine, none of them generally manage the job verywell. In diabetes-computing there have been a whole plethora ofprototype decision-support systems - but few if any successfulproducts. While things may be changing, as should be apparentfrom the diversity of approaches reported in the SpecialIssues, it would be over-simplistic to believe that a singlecomputational technique will work for all patients. Differentdata requirements and demands on patients mean that differentmethodologies may be more or less suited to individual patients.Each approach, however, must be formally validated and clinicallyevaluated. Achieving this in practice is not without itsdifficulties.

CALL FOR EVALUATION

The testing of IT prototypes remains thesubject of much debate, not just in diabetes-computing, but inmedical informatics generally. For example whether an evaluationmodel based on a drug trial is appropriate still needs to beestablished. As overviewed in the Computers inDiabetes’96 Meeting Conference Report (48), which can be found in part 2 of the Special Issue (6),when a CPN-based diabetes advisory system was tested in 12patients it ended up being evaluated as a decision system ratherthan a decision support system. It is obviously far fromideal if the process of evaluation itself affects theproposed use of the system under evaluation. Similarly, torigorously evaluate the HumaLink system in a‘blinded’ fashion could be problematical. Getting acontrol group to telephone a dial-in service with nodiabetes-related content a number of times per day could bedifficult to sustain over a long period of time. Obviously itwould be possible to have a control group who only use HumaLinkas a data collection device - recording their BG measurements -but not receiving any advice back. Conceptually this might allowthe added benefit of the semi-‘intelligent’ componentof the system - the algorithms - to be evaluated separately.Although once again it needs to be ascertained whether patientswould sustain the use of such a system long-term, withall its demands, without any reward or perceived benefit.

It all clearly depends on whether one wants toshow that a particular computational approach does somegood or whether it is sufficient to demonstrate that the whole ITprocess is good for diabetes care. If telephoning a centralcomputer and typing in BG values can make patients think moreabout their diabetes and improve their glycaemic control - andthis can be demonstrated in a range of randomisedstudies in a wide variety of patients in different centreslong-term - this would be a useful intervention. This isregardless of whether it is the algorithms or the act oftelephoning which are improving BG control. In this respect weshould perhaps not focus solely on evaluating the technologybut rather the process. After all we do not want to losesight of what we are trying to achieve, namely an improvement inglycaemic control and patient care.

Piwernetz et al (49) commented six years ago in an Editorial in thisjournal that: "The biggest challenge for each system andthe unquestioned prerequisite is, however, its evaluation. In thefield of evaluation more work needs to be done. This requires anagreed methodology for scientific evaluation of the impact ofcomputer systems on health care. Feasibility and implementationstudies cannot simply follow randomized, double-blind, cross-overdesigns, it is essential to apply more complex protocols".This appears to be as true now as it was when written.Unfortunately not much practical progress seems to have been madein the past six years. In this respect one of the most pressingissues which taxed referees when reviewing submissions for theSpecial Issues (5,6) was in what way should such systems be evaluated andhow should the results be presented. Cynics might argue that bynot exclusively using randomised, blinded controlled trials weare simply trying to move the goal posts to suite the needs ofour computer systems. This author disagrees. While randomisationcan easily be achieved, and therefore should be undertaken -blinded, placebo controlled trials are not feasible in every areaof medicine (e.g. surgery). Such ‘gold standard’evaluation procedures also may not be ideally suited forassessing currently available decision-support prototypes. Whilepurists may demand randomised, blinded controlled trials to beconvinced of a system’s clinical utility, it needs to berecognised that such demands may be difficult to address, forreasons other than whether a system offers substantial clinicalbenefit. Given this, there is a very real risk that such demandscould unnecessarily restrict the wider utilisation of prototypeswhich might otherwise have promising clinical potential.Nevertheless concerns over evaluation methodologies will need tobe addressed, before software - such as that reviewed here - willsee widespread utilisation and clinical acceptance.

CONCLUDING REMARKS

So what conclusions can be drawn about theapplication of IT in clinical diabetes care, as of early1997? BG meters and insulin pumps are well established tools. Theuse of database software is also widely accepted now.Furthermore, quite a wide variety of educational games andprograms for patient use at home are now available commercially.Some of these have started to be formally evaluated - but manyhave not. Decision support research has not yet come to fruitionalthough promising approaches are starting to be tested. In thisrespect, most of the papers in the Special Issues (5,6) do not propose, at present, to manage complicatedcases using computer-based decision making tools - i.e withouthuman input. Rather the consensus seems to be to focus onhandling reasonably straight-forward clinical cases with acomputer - freeing up more of clinicians’ and health-careprofessionals’ limited time to devote to the more difficultcases (47).

Looking to the future, most currentdecision-support prototypes provide a feedback loop which isclosed only at discrete times rather than continuously.Non-invasive glucose monitoring could help to change this -offering potentially much more frequent data sampling which couldrevolutionise the provision of modern day diabetes care.Furthermore what many diabetic patients - especially teenagers -fear most are not the later life complications of their diabetes,but rather quite understandably going ‘hypo’.Hypoglycaemia alarms to alert patients to the possibility ofnocturnal hypoglycaemia, although still at the research stage,could be another way in which computers may possibly be of directbenefit to patients in the future.

How can further advances in diabetes-computingbe ensured? There is a very real need for greater co-ordinationof endeavours in this field. Clinically a multifactorial approachis required, combining concerted efforts at tightening BG controlwith improved patient education. Computers can help with boththese processes. However, as should be self-evident from theforegoing, on its own measuring BG will not achieve improvedglycaemic control. Active interventions are required.

In conclusion, the way forward indecision-support and education in diabetes care is likely to bethrough integrated IT developments built on collaboration.Special attention will also need to be devoted to evaluationissues. Although the MedicalInformatics Special Issues (5,6) should not be considered as in any way comprehensivein their coverage of clinical diabetes computing - it is hopedthat the compilation of papers provided there - along with thiscritical appraisal - may offer a useful source of novel ideas -as well as, perhaps, a starting point for future research.

ACKNOWLEDGEMENTS

The author thanks Professor Enrique Gomez(Madrid, Spain), Dr. Michael Albisser (Miami, Florida, USA),Steve Brown (Raya Systems Inc., California, USA), and Dr. JohnDay (Ipswich, UK) for their kind permission to utilisefigures / data from their Medical Informatics papers. Very specialthanks are also extended to Taylor & Francis, publishers ofMedical Informatics, for their kind support of thediabetes-computing Special Issues - further details of which canbe found here on the World Wide Web.

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• Please note that abstracts for the papers published in the Special Issues of Medical Informatics can be accessed at: http://www.ncbi.nlm.nih.gov/PubMed

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