Вопрос 14-3/2: Информация и электросвязь/икт для электронного здравоохранения


Japan: Case 3: Mobile Support Tool for Doctors



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2.4 Japan: Case 3: Mobile Support Tool for Doctors

2.4.1 Introduction


0EMR systems are becoming popular in medicine (A.L.Rector. 1996, Anderson JD. 1999, David W. Bates et al. 2003, Samuel J. Wang et al. 2003, Jim Johnson. 2010). This is because such systems enable doctors to manage mass medical data easily. As represented by POMR (Weed LL. 1968), medical record systems have been expected to support doctors’ planning. However, most of these EMR systems are only capable of performing electronic data storage of legacy medical records. To help doctors analyze medical data that are generated over time, the system has to have the ability to present medical data that occur over various time spans. Because medical data often occur over various time spans, doctors have to study it over various time spans when performing a medical analysis. With current systems, doctors can look at medical data for only a few days at most. Accordingly, they cannot analyze medical data effectively. In addition, there is another problem that doctors do not have much time to use the EMR at their desks. As a result of these problems, doctors require a system that can support their cognition and medical analysis regardless of where they are. In view of these problems, we developed a brand new EMR system that supports doctors’ understanding and medical analysis. This system has the features listed below:

 It has the ability to present medical data that occurs chronologically over various time spans.

 Its client application works on mobile devices such as a mobile phone or a tablet PC.

 In spite of the narrow bandwidth of wireless mobile networks, the system responds quickly.

In this paper, we introduce the conventional EMR systems in section 2 and explain their problems. In section 3, to solve these problems, we introduce the Mobile Timeline EMR System and its technological features.

2.4.2 Conventional EMR Systems


In this section, we introduce the conventional EMR systems. Typically, a conventional EMR system has a user interface similar to legacy medical records written on paper. Also, it displays the patients’ SOAP information and the patient’s information for one day. With these systems, by treating medical data as electronic data, doctors can search and manage their patients’ medical data easily. This ability of mass data management is a significant advantage compared with legacy medical records. However, there are various kinds of medical data that are generated over various time spans. Thus, with these systems, which can make medical data available for only a few days, doctors cannot always look at it and infer the relationships that may exist among the various data. In other words, though doctors can look at and understand the state of patients who come for consultations two or three times with these systems, doctors are unable to examine and understand the state of patients who may have been suffering from certain conditions for years, such as asthmatics, diabetics or patients suffering from hypertension. Accordingly, these systems are unable to attain the purpose of supporting doctors’ analysis and understanding.

2.4.3 Mobile Timeline EMR System


As described above, in order to meet the need to support doctors’ understanding and medical analysis, the system must have a function to present medical data that are generated over various time spans and allow doctors to look at medical data from any perspective. In order to solve this problem, we have introduced a timeline interface. The timeline interface has a multistage time scale including years, months and days. With a timeline interface, the system can present chronological data over various time spans.

In addition, in order to meet the demands for mobility and portability, we use a mobile device as a client of this system. By adopting a mobile device, the system gains a significant advantage in that doctors can analyze data anywhere. However, mobile devices have the problems listed below:

 Difficulty with input and reading using a small display.

 Low data transmission rate through a mobile wireless network.

To solve problem (1), we adopted an advanced word completion function using an optimized lexicon for each field of medicine.

For problem (2), we implemented the Adaptive Event Merge algorithm that reduces data transmission.

By means of the above, we can create a tool that is capable of supporting doctors’ understanding and analysis wherever they are.

2.4.4 Timeline Interface


The timeline interface is the most important part of this system. With timeline interface, doctors can change the time scale to various time units. For example, doctors can change the unit time scale from hour to day or to month. By changing to a smaller time unit, doctors can observe medical data over a short time span in detail. Conversely, by changing to a longer time unit, doctors can look at medical data over a long time span. The length of the time unit can also be changed by pinch-in/pinch-out. The reasons why we use this interface are presented below:

There are various kinds of medical data. They occur over various time spans and they are interrelated in various ways.

If the different data are interrelated, the appropriate time scale for observation can be chosen. By selecting the appropriate time scale, the system can elucidate the relationships among the data.

For example, take the case where relationships can be discerned when data are observed over a long time span, where this would not be case if observation were to occur over a short time span. Conversely, there are other cases where relationships are not apparent when data are observed over an excessively long time span. Accordingly, the system must have a function that allows users to select the appropriate time scale.

With this timeline interface, doctors can change the time scale at will. Therefore, the various relationships between various data can be observed. In other words, doctors can examine medical data from various points of view.

In this manner, this system can serve not only as a tool for managing medical data, but can also support understanding and medical analysis.


Word completion using lexicon for medical data


The input method is not only an important factor that decides the usability of the system on mobile devices, but also a difficult problem. This is because mobile devices only have poor input accessories such as small touch panels and keyboards. In particular, in EMR systems, doctors have to input special characters for medical treatment using these poor input devices to write down the SOAP information or to search patients. To solve this problem, the word completion method using a lexicon of medical words is well known and effective (Laird S. Cermak et al 1992, C. G. Chute et al 1999, Hiroyuki Komatsu et al 2001). However, the words used in medicine differ significantly among the different fields. In other words, using the same lexicon for all the different areas of medicine would be limiting and inadequate. For example, the phrase ‘nephrotic syndrome’ is often used by paediatricians, but is rarely used by ophthalmologists. Accordingly, we have optimized the lexicon for each field of medicine. Simply put, we changed the bias of the TRIE (Donald R. Morrison 1968) structure of the lexicon for each field. Then for each field, by summarizing and analyzing the most common inputs from doctors in a particular field, the system succeeded in improving the accuracy of word completion.

Adaptive event merge algorithm


The response speed of the system is a very important factor for deciding the system’s usability. The advantage of the timeline interface is, as we described above, the ability to visualize the relationships among medical data by varying the time scales. If doctors want to look at data over a long period, they can expand the time scale as they wish. However, the system has to display a lot of data objects at once. On the other hand, in this system, since its client is a mobile device, the client only has narrow wireless communication bandwidth. In order to improve the response speed in this system, we implemented an Adaptive Event Merge algorithm. This algorithm is a function that merges neighbouring data objects adaptively.

When doctors expand the time scale, if the time gap of the neighbouring data objects is smaller than the threshold, the system merges the objects into one object. In this manner, the system can reduce the amount of data transmitted and improve the response speed and usability.


2.4.5 Conclusion


In this paper, we introduced an EMR system based on a new concept. This system has a significant advantage in that it is able to support doctors’ understanding and medical analysis wherever they are. The system has the three features described below. With the timeline interface, doctors can look at and analyze medical data using various time scales. This is a significant help for doctors’ understanding and analysis. Since its client is a mobile device, the system can support doctors wherever they are. In spite of using a mobile device as a client, the system can respond very quickly. In addition to the features described above, as the system is easy to use, there is a possibility that the doctors can use it as an educational tool.

References

[1] A.L.Rector., 1996. Computer-based Patient Records, Yearbook of Medical Informatics, Section 2, 195-198.

[2] Anderson J.D., 1999. Increasing the acceptance of clinical information systems. MD Computing 16(1)62-5.

[3] David W. Bates, Mark Ebell, Edward Gotlieb, John Zapp, and H.C. Mullins., 2003. A Proposal for Electronic Medical Records in U.S. Primary Care, J Am Med Inform Assoc.

[4] Samuel J. Wang, Blackford Middleton, Lisa A. Prosser, Christiana G. Bardon, Cynthia D. Spurr, Patricia J. Carchidi, Anne F. Kittlera, Robert C. Goldszer, David G. Fairchild, Andrew J. Sussman, Gilad J. Kuperman, David W. Bates,2003. A cost-benefit analysis of electronic medical records in primary care. The American Journal of Medicine.

[5] Jim Johnson, 2010. Making a Successful Transition to Electronic Medical Records. Healthcare Technology Online.

[6] Weed LL., 1968. Medical records that guide and teach. New England Journal of Medicine 278:593-600.

[7] Laird S. Cermak, Mieke Verfaellie, Marie Sweeney and Larry L. Jacoby, 1992. Fluency versus conscious recollection in the word completion performance of amnesic patients, Brain and Cognition Volume 20, Issue 2, Pages 367-377.

[8] C. G. Chute, P. L. Elkin, D. D. Sherertz, and M. S. Tuttle,1999. Desiderata for a clinical terminology server, Proc AMIA Symp.

[9] Hiroyuki Komatsu, Akira Takabayashi, Toshiyuki Masui, 2001. Predictive Text Input with Japanese Dynamic Abbreviation Expansion Method “Nanashiki”, WISS.

[10] Donald R. Morrison, 1968. PATRICIA--Practical Algorithm to Retrieve Information Coded in Alphanumeric, jacm


Каталог: dms pub -> itu-d -> opb -> stg
stg -> Вопрос 17-3/2: Ход деятельности в области электронного правительства и определение областей применения электронного правительства в интересах развивающихся стран
stg -> Вопрос 10-3/2: Электросвязь/икт для сельских и отдаленных районов
dms pub -> Рекомендация мсэ-r m. 1036-4 (03/2012)
stg -> Вопрос 7-3/1: Внедрение универсального доступа к широкополосным услугам
stg -> Вопрос 19-2/1: Внедрение основанных на ip услуг электросвязи в развивающихся странах


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