Use of IT in Diabetes Care
(Re)-Load AIDA Frames / Menus
Reprinted with the kindpermission of Editrice Kurtis, publishers of Diabetes, Nutrition &Metabolism: Clinical andExperimental
Application of computers inclinical diabetes care
ABSTRACT. This articlecritically appraises selected clinically related papers thatrecently appeared in a two-part Special Issue of
Diab. Nutr. Metab. 10: 45-59, 1997.
© 1997, Editrice Kurtis.
Academic Department of Radiology, The RoyalHospitals NHS Trust, St. Bartholomew’s Hospital, London andDepartment of Imaging, National Heart and Lung Institute(Imperial College of Science, Technology and Medicine), RoyalBrompton Hospital, London, U.K.
Key words: Diabetes mellitus, informationtechnology, computers, education, decision support, telemedicine,simulations, games.
Correspondence to: Dr. E.D. Lehmann, MR Unit,NHLI, Royal Brompton Hospital, London SW3 6NP, U.K.
Received 15 February 1997; accepted 30 April1997.
1997 is the 75th anniversary of the publicationof Frederick Banting and colleagues’ keynote paper in the CanadianMedical Association Journal (
"It is not always easy to adjust so that there is sufficient insulin to nullify post-prandial hyperglycaemia and yet insufficient insulin to produce dangerous lowering of the blood sugar".
Not much has changed in 75 years. At around thesame time as Banting and Best were reporting their preliminaryfindings (3
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 (
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 Other novel work - as yet unpublished in the medicalcomputing / diabetes literature - was also sought for inclusion;the aim being to produce two volumes describing new and excitingwork in this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 (
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.
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.
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) (
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 (
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
Initiatives to reduce the suffering associatedwith diabetes mellitus include the St. Vincent Declaration (
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
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
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 (
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
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 (
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 (
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 (
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 (
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 (
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
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 (
Albisser and colleagues (
Also, while the overall numbers of patientsstudied were large, and involved we are told in total 888 patientmonths of prospective follow-up, HbA1c 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 HbA1cdata on all the patients studied, lest biases be introduced bythose subjects ‘lost’ to follow-up or for whom suchrepeat HbA1c 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 HbA1cdata 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 HbA1clevels, such as those reported by Albisser et al (
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).
"Glucose measurements are futile ifnot acted upon"
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 (
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 (
As Chao (
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 (
One particular simulator, called AIDA (
The issue of evaluating educationalinterventions was also addressed in the following paper in part 2of the Special Issue by Brown et al (
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 HbA1c were not demonstrated the authors quitecorrectly highlighted that the patients were reasonablycontrolled at the start (mean baseline HbA1c :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
It is not possible to cover in this review allof the 15 papers included in the
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 (
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 (
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 (
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 (
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
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
1. Banting F.G ., Best C.H., Collip J.B., Campbell W.R., Fletcher, A.: Pancreatic extracts in the treatment of diabetes mellitus: a preliminary report. Can. Med. Assoc. J. 12: 141-146, 1922.
2. Banting F.G ., Campbell W.R., Fletcher A.A.: Further clinical experience with insulin in the treatment of diabetes mellitus. Br. Med. J. 1: 8-12, 1923.
3. Banting F.G., Best C.: The internal secretion of the pancreas. J. Lab. Clin. Med. 7: 251-266, 1922.
4. Chao S.C.E .: A recording simulator for teaching the self-management of diabetes mellitus. MSc Thesis. University of Toronto, Canada, 1990.
5. Lehmann E.D. : ed. Special Issue: Application of information technology in clinical diabetes care. Part 1. Databases, algorithms and decision support. Med. Inf. 21: 255-378, 1996.
6. Lehmann E.D. : ed. Special Issue: Application of information technology in clinical diabetes care. Part 2. Models and education. Med. Inf. 22: 1-120, 1997.
7. Lehmann E.D. , Deutsch T.: Application of computers in diabetes care - a review. I. Computers for data collection and interpretation. Med. Inf. 20: 281-302, 1995.
8. Lehmann E.D. , Deutsch T.: Application of computers in diabetes care - a review. II. Computers for decision support and education. Med. Inf. 20: 303-329, 1995.
9. Lehmann E.D.: Application of information technology in clinical diabetes care - a Special Issue. Part 1. Databases, algorithms and decision support [Editorial]. Med. Inf. 21: 255-258, 1996
10. The Diabetes Control and Complications Trial Research Group.: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329: 977-986, 1993.
11. The Diabetes Control and Complications Trial Research Group.: Resource utilization and costs of care in the Diabetes Control and Complications Trial. Diabetes Care 18: 1468-1478, 1995.
12. Harris M.I .: Testing for blood glucose by office-based physicians in the U.S. Diabetes Care 12: 419-426, 1990.
13. Eastman R.C ., Siebert C.W., Harris M., Gorden P.: Implications of the Diabetes Control and Complications Trial. J. Clin. Endocrinol. Metab. 77: 1105-1107, 1993.
14. Moy C.S .: ed. Diabetes 1993 vital statistics. New York, American Diabetes Association, 1994, pp. 43.
15. King’s Fund Policy Institute .: Counting the cost: the real impact of non insulin dependent diabetes. British Diabetic Association, London, 1996.
16. EURODIABETA .: Information technology for diabetes care in Europe: the EURODIABETA initiative. Diabet. Med. 7: 639-650, 1990.
17. Lehmann E.D. : The Diabetes Control and Complications Trial (DCCT): A role for computers in patient education? Diab. Nutr. Metab. 7: 308-316, 1994.
18. WHO/IDF Europe.: Diabetes Care and Research in Europe. The Saint Vincent Declaration. Diabet. Med. 7: 360, 1990.
19. Gorman C ., Looker J., Fisk T., Oelke W., Erickson D., Smith S., Zimmerman B.: A clinically useful diabetes electronic medical record: Lessons from the past; pointers toward the future. Eur. J. Endocrinol. 134: 31-42, 1996.
20. Flack J.R. : The impact of information technology on diabetes patient care. Diab. Nutr. Metab. 9: 145-159, 1996.
21. Piwernetz K., Home P.D., Snorgaard O., Antsiferov M., Staehr-Johansen K., Krans M., for the DIABCARE Monitoring Group of the St. Vincent Declaration Steering Committee.: Monitoring the targets of the St. Vincent Declaration and the implementation of quality management in diabetes care: the DIABCARE initiative. Diabet. Med. 10: 371-377, 1993.
22. Kopelman P.G., Sanderson A.J.: Application of database systems in diabetes care. Med. Inf. 21: 259-271, 1996.
23. Engelbrecht R., Hildebrand C., Brugues E., de Leiva A., Corcoy R.: DIABCARD - an application of a portable medical record for persons with diabetes. Med. Inf. 21: 273-282, 1996.
24. Gomez E., del Pozo F., Hernando E.: Telemedicine for diabetes care: the DIABTel approach towards diabetes telecare. Med. Inf. 21: 283-295, 1996.
25. Bellazzi R ., Cobelli C., Gomez E., Stefanelli M.: The T-IDDM project: Telematic management of Insulin Dependent Diabetes Mellitus. In: Bracale M., Denoth F. (Eds.), Proceedings HT-95, Ischia, 1995, pp. 271-276.
26. Fallucca F ., Di Biase N., Sabbatini A., Borrello E., Sciullo E., Napoli A.: Telemedicine in the treatment of diabetic pregnancy. Pract. Diab. Intl. 13: 115-118, 1996.
27. Rosenfalck A.M., Bendtson I.: The Diva System, a computerized diary, used in young type 1 diabetic patients. Diabete Metab. 19: 25-29, 1993.
28. Lehmann E.D ., Deutsch T.: Computer assisted diabetes care: a 6 year retrospective. Comput. Methods Programs Biomed. 50: 209-230, 1996.
29. Albisser A.M ., Harris R.H., Sakkal S., Parson I.D., Chao S.C.E.: Diabetes intervention in the information age. Med. Inf. 21: 297-316, 1996.
30. Albisser A.M.: Intelligent instrumentation in diabetic management. CRC Crit. Rev. Biomed. Eng. 17: 1-24, 1989.
31. Day J.P .: Some considerations of legal liability concerning the use and future development of knowledge based or expert systems in diabetes care. Diab. Nutr. Metab. 8: 195-200, 1995.
32. Brahams D ., Wyatt J.: Decision-aids and the law. Lancet ii: 632-634, 1989.
33. Skyler J., Skyler D., Seigler D., Sullivan M.O.: Algorithms for adjustment of insulin dosage by patients who monitor blood glucose. Diabetes Care 4: 311-318, 1981.
34. Worth R., Home P.D., Johnston D.G., Anderson J., Ashworth L., Burrin J.M., Appleton D., Binder C., Alberti K.G.M.M.: Intensive attention improves glycaemic control in insulin-dependent diabetes without further advantage from home blood glucose monitoring: results of a controlled trial. Br. Med. J. 285: 1233-1240, 1982.
35. Daneman D. , Siminerio L., Transue D., Betschart J., Drash A., Becker D.: The role of self-monitoring of blood glucose in the routine management of children with insulin-dependent diabetes mellitus. Diabetes Care 8: 1-4, 1985.
36. Coonrod B.A ., Betschart J., Harris M.I.: Frequency and determinants of diabetes patient education among adults in the US population. Diabetes Care 17: 852-858, 1994.
37. Assal J.P.: The Diabetes Education Study Group of the European Association for the Study of Diabetes. 15 years devoted to improving patient management. Medicographia 17: 15-20, 1995.
38. Assal J.P., Ekoe J.M., Lacroix A., Liniger C.: Teaching patients about their disease and its treatment: therapeutic success, professional failure. In: Sakamoto N., Alberti K.G.M.M., Hotta N. (Eds), Recent trends in management of diabetes mellitus, Excerpta Medica, Amsterdam, 1987, pp. 63-74.
39. Lehmann E.D .: Interactive educational simulators in diabetes care. Med. Inf. 22: 47-76, 1997.
40. Biermann E. , Mehnert H.: DIABLOG: a simulation program of insulin glucose dynamics for education of diabetics. Comput. Methods Programs Biomed. 32: 311-318, 1990.
41. Lehmann E.D .: Diabetes moves onto the Internet. Lancet 347: 1542, 1996.
42. Lehmann E.D ., Deutsch T., Broad D.: AIDA: an educational simulator for insulin dosage and dietary adjustment in diabetes. 1996. Available from: http://www.2aida.org
43. Lehmann E.D. , Deutsch T., Broad D.: AIDA: an educational simulator for insulin dosage and dietary adjustment in diabetes. British Diabetic Association, London, 1997.
44. Dammacco F ., Frezza E., Torelli C., Pacifico A., Buono A.: Comparative study of teaching methods for diabetic children: superiority of computer-assisted instruction. In: Sakamoto N., Alberti K.G.M.M., Hotta N. (Eds), Recent trends in management of diabetes mellitus. (eds.), Excerpta Medica, Amsterdam, 1987, pp. 610-613.
45. Brown S.J. , Lieberman D.A., Gemeny B.A., Fan Y.C., Wilson D.M., Pasta D.J.: Educational video game for juvenile diabetes: results of a controlled trial. Med. Inf. 22: 77-89, 1997.
46. Day J.L ., Rayman G., Hall L., Davies P.: "Learning Diabetes" - a multi-media learning package for patients, carers and professionals to improve chronic disease management. Med. Inf. 22: 91-104, 1997.
47. Lehmann E.D. : Application of information technology in clinical diabetes care - a Special Issue. Part 2. Models and education [Editorial]. Med. Inf. 22: 1-3, 1997.
48. Lehmann E.D .: Computers in Diabetes’96. Med. Inf. 22: 105-118, 1997.
49. Piwernetz K., Massi Benedetti M., Bauersachs R., Sönksen P.H.: Foreword. Diab. Nutr. Metab. 4 (Suppl. 1): 1, 1991.
Return to Top of Page If you like AIDA, why not display our logo on your home page? For more information about linking to the AIDA Website please click here.
Return to AIDA Website Home Page AIDA is a freeware diabetes software simulator program of glucose-insulin action + insulin dose & diet adjustment in diabetes mellitus. It is intended purely for education, self-learning and / or teaching use. It is not meant for individual blood glucose prediction or therapy planning. Caveats
This Web page was last updated on 9th December, 2000. (c) www.2aida.org, 2000. All rights reserved. Disclaimer. For the AIDA US Mirror Site, please click here. For the Diabetes / Insulin Tutorial, please click here.