Data

The data available from point-of-care devices is reviewed here in terms of its accuracy, its efficiency in achieving desirable clinical outcomes, its relevance, data storage and transfer issues. Also considered are issues related to the interoperability of data as it is exchanged.

50 2.3.1.1 Accuracy of data

Ho et al. (2004) evaluated five glucometers used in neonatal care for their accuracy in detecting low blood glucose in newborns. Data generated on the device was sufficient to conclude that results could not be used exclusively and a laboratory test was still required (Ho et al. 2004).

Weitgasser et al. (1999) carried out a study comparing older generation with more recent glucometers, specifically comparing their accuracy and precision. The authors used error-grid analysis to determine data accuracy. Error-grid analysis is clinically most relevant when considering accuracy of glucometers. More modern glucometers were more accurate, despite requiring smaller blood volumes. This was attributed to technological improvements. Although no details of these improvements were provided, improved sensitivity of the test is likely to be the most important factor. Randolph et al. (2000) conducted a study considering whether results obtained from the i-STAT blood analyzer would be consistent and accurate irrespective of its location of use. This was undertaken to check its suitability in giving accurate results when transferring patients between hospitals. In order to check the precision of results, duplicate analysis of samples from patients were carried out prior to and during the transfer. Data generated on the device was sufficient for interpretation by the users and it was not analysed further.

Cohen et al. (2006) compared the accuracy and precision of glucometers used in Australia. Accuracy was determined by simultaneously analysing blood samples from the patient using a reference method and the glucometers. Precision was determined through the calculation of the coefficient of variation of the control solutions. The authors also noted that precision and accuracy were both significantly improving in more recent generations of glucometers.

51 2.3.1.2 Efficiency of generating data

Kendall et al. (1998) investigated whether the clinical outcome of patients was affected when a point-of-care blood analyzer was used compared with the hospital laboratory in the accident and emergency department of the hospital. Details were not provided of whether data analysis occurred directly on the device. Using the point-of-care blood analyzer reduced the time doctors spent waiting for results of blood tests. The shorter time however did not improve the clinical outcome of patients thus indicating that the time taken to wait for results did not have an impact on the treatment of patients (Kendall et al. 1998).

Prause et al. (1997) investigated whether pre-hospital patient care could be improved through the use of a point-of-care device for measuring blood gases. The device was used by health professionals working in emergency medicine. The results obtained showed that they could manage cardiac failure more quickly because data was provided quickly and reliably from the device. A typical test for electrolytes took 70 s while obtaining blood gases took 160 s. There were some cases (although the number was not given) when recalibration of the device was needed and this took a further 25 s.

Additional features such as faster test results (between 20 – 60 s) and memory functionality (storage for up to 300 results) and a smaller size were noted to describe the enhancement of new generation glucometers compared with old ones (Weitgasser et al. 1999).

Evidence on the timeliness of data related to the speed of generating results on devices; however the key aspect of this depends on whether users of point-of-care devices act on the data quickly.

2.3.1.3 Relevance to test requirements

Salustri and Trambaiolo (2002) described a hand-carried cardiac ultrasound (HCU) device which provided 2-D details on features of the heart. These included: size of

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the chambers, ventricular hypertrophy (which can result from hypertension) and ejection fraction; size and dynamics of inferior vena cava. In a case-study comparing measurements of standard echocardiography and HCU for screening hypertensive patients, Salustri and Trambaiolo (2002) concluded that HCU devices could be used for diagnosing ventricular hypertrophy because sufficient detail was provided on the device to enable health professionals to interpret the images.

Biehl and Velten (2008) indicated that generic challenges within primary care affecting the development of point-of-care devices are related to developing precise and ‗robust‘ devices that are straightforward to use. Biehl and Velten (2008) also add that automatic calibration and the ability to test multiple components are advantageous.

2.3.1.4 Storage of data

Burkhart et al. (2007) carried out a study over a period of 16 weeks evaluating how regular peak flow monitoring would affect children suffering from asthma. The authors found that the children‘s awareness of their condition was increased since the following were reduced: the number of asthma episodes, the number of visits to the health professional or clinic due to asthma problems and the number of school days missed. The Accutrax personal diary spirometer was used to measure peak expiratory flow and forced expiratory volume in 1 second. The device recorded both the date and time and provided storage facilities for the results although users were asked to manually record the data into a diary initially.

A report by PDS/Ferraris Respiratory (2004) described a hand held lung function monitor which could measure the peak expiratory flow (PEF) to indicate whether the user was likely to face an asthma attack. One of the features present on the monitor was memory facilities to store the results.

The data from the GlucoPhone (an integration of a glucometer and a mobile phone) was stored in a server that was linked to a website which was available to patients, parents and health professionals (Carroll et al. 2007).

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A recent review of 100 glucometers by the authors investigating informatics on modern glucometers available between 1998 and 2008 showed that all glucometers had memory facilities thus indicating that blood glucose results do not need to be manually recorded by users (Ajai et al. 2009). In addition to blood glucose results, averages were calculated based on the stored values and these ranged from 7, 14, 21, 30, 60 and 90- day averages.

2.3.1.5 Transfer of data from devices

The Accutrax personal diary spirometer used in the Burkhart et al. (2007) study had the capability for results to be downloaded to a computer for further analysis, after users had been trained. However, the medium of downloading was not noted in the study (i.e. via wireless or wired connections).

Cohen et al. (2006) noted that the download feature on glucometers was being used thus removing the need for manual recording of the data in a logbook. This demonstrated that further data analysis could be done to provide information to users on the trends identified within their data. A caveat was given that users however needed to maintain the right date and time on their glucometer to ensure its accuracy. There was a tendency that the date and time needed to be reset the first time the glucometer was used and when batteries were changed.

A pilot study considering a prototype device known as the GlucoPhone which helped adolescents in monitoring blood glucose levels was investigated by Carroll et al. (2007). The authors established that users found the device easy to use and it helped in better diabetes management. On carrying out the test, data was initially displayed on the phone before it was transferred to a server.

A hand held lung function monitor by PDS/Ferraris Respiratory (2004) also had the capability to download results to external software where trends could be seen from the data.

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Prause et al. (1997) explained that the blood analyzer had the functionality to download the results thus providing the scope for data to be manipulated away from the device however details of how the data was utilised was not given.

Apostolopoulos et al. (2007) conducted a study to investigate the usability of technology in aiding patients in self management of diabetes. To transfer the results to the health professionals, patients were given the option of either downloading their results themselves, sending it via the internet, phoning the health professional at the clinic with their results or sending their results via SMS using a mobile phone. Data was not further analysed on the device hence the need for a software application. Finkelstein et al. (2000) described asthma patients using a spirometer which measured the volume of air inspired and expired by the lungs. Users initially entered answers to questions which categorised their symptoms based on a scoring method learnt by them into a palmtop computer. On completing the questions, users were prompted to perform spirometry tests. A button on the spirometer downloaded the results into a palmtop computer. Data was also transferred to a central clinical information system for further analysis.

The OneTouch UltraSmart glucometer allowed results to be downloaded from the device to a PC where software (OneTouch Diabetes Management Software) was used to further manipulate the data.

A non-invasive glucose monitor known as the ‗GlucoWatch Biographer‘ was approved by the FDA (Food and Drug Administration) in 2001 (FDA, 2001). However it is no longer being manufactured therefore limited information was available. It was designed to complement rather than replace finger-prick tests. Its purpose was to obtain glucose levels readings at regular intervals (i.e. every 20 minutes) and track patterns therefore detecting trends. It had a capability to transfer results to a computer.

55 2.3.1.6 Interoperability of devices

Garguilo et al. (2007) examined interoperability in the context of medical devices for acute and continuing care within the hospital environment. The authors identified that a generic communication standard was needed. However, they noted that several standards existed for different layers for communication of devices and medical systems. Schrenker and Cooper (2001) noted that in order for devices to exchange data, ―they must speak the same language grammatically and semantically‖.

Standards exist for communication of medical devices and these are classified under the ISO/IEEE 11073 family of standards. The ISO/IEEE 11073 standard defines the ―information model‖, which includes the device attributes, range of values and the way data is accessed. Part 10417 of the ISO/IEEE 11073 standard defines communication for plug-and-play interoperability between glucometers and data handling devices such as mobile phones, computers, personal health appliances and set-top boxes (ISO/IEEE, 2009). Part 30200 of the ISO/IEEE 11073 standard, defines a standard for medical devices that connect with a cable to a data handling device (ISO/IEEE, 2004a). Part 30300 of the ISO/IEEE 11073 standard defines a standard for wireless communication through infrared (ISO/IEEE, 2004b). Although the ISO/IEEE 11073 standard is for new devices, existing devices can also be modified to comply with it.

These standards are targeted at ensuring that data obtained from medical devices can be ―archived, retrieved and processed‖ using a range of applications without the need of a wide range of software for data conversion and without compromising data integrity or misplacing data.