*Results from Telehealth DOI: http://dx.doi.org/10.5772/intechopen.101183*

#### **Figure 28.**

*Effect of telemonitoring on CHF hospital admission [39].*

#### *5.3.2 Emergency room visits*

The review [39] found seven studies that provided the number of emergency visits as secondary outcome (**Figure 29**); but with mixed outcome. Some studies observed no significant difference in emergency visits among the patients in treatment and control arm (P = 0.43); some reported lower emergency contacts were observed; yet others reported an increase in emergency room visits. Pooled results of data extracted from 4 studies showed no significant reduction in risk of emergency admission between the groups (1.04, 0.86 to 1.26). Heterogeneity exited as P = 0.001, I<sup>2</sup> = 82%.

#### *5.3.3 Length of stay*

The review [39] found nine studies that evaluated the effect of intervention on length of stay in hospital. Among these, seven studies showed no difference in length of stay at hospital due to any cause or CHF among the patients across both groups. Two studies reported reduction in length of stay among patients in home monitoring group due to CHF.

However a more detailed analysis on length of stay [48] reveals that patients with CHF who are admitted to hospital often present a complex set of issues. Patients sometimes develop complications and so remain in hospital for an extended period; likewise, earlier admissions may have been protracted, but stays during the trial are significantly shortened; this results in a significant spread in length of stay (**Figure 30**). Overall a reduction in length of stay is observed. This is often attributed to early discharge being possible as the patient feels safe to return

#### **Figure 29.**

*Effect of telemonitoring on all-cause emergency room visit [39].*

#### **Figure 30.**

*Change in length of stay for each patient compared to length of stay in the period immediately before monitoring [48].*

home because they are being monitored. There may be an affect due to the earlier planned admission and the prompt intervention results in reduced recovery time.

#### *5.3.4 Cost*

Two of the studies in the meta-analysis [39] evaluated the cost effectiveness of a telemonitoring based intervention against the usual care but neither determined that there was a reduction. One study on cost-effectiveness [48] undertook evaluation on two separate organisations (Primary Care Trusts) and found wide discrepancy in outcomes, indicating why the literature is inconclusive. The study determined an average annual saving of £1023 per patient, but this was highly influenced by factors pertaining to the way in which the service was established and patients were managed.

## **6. Future developments**

#### **6.1 Monitoring activities of daily living**

An area of development for telehealth is in monitoring the activities of daily living (ADL), especially of the frail elderly. Changes in behaviour may be used to detect early deterioration in health and before it may be evident in other symptoms, which may also be being monitored by telehealth. This remains a little researched area, with almost all reported work being on development of sensors [65]. However, one of the few studies that reports ADL and correlates with clinical events [45] presents evidence that shows that deviation from an established, characteristic behaviour pattern can indicate deterioration in a health condition and impending clinical event. Anecdotal evidence suggests patients may tire easily and thus rest more in a chair; have altered sleeping patterns; become restless at night; sleep in the day; visit the toilet more frequently; and take longer to complete everyday activities. Simple sensors such as motion and bed/chair occupancy and analysis of their data can be used to monitor these events, patterns of normal behaviour can be established, and deviation can be recognised.

**Figure 31.** *Time of first and last detected motion in living room [45].*

In one example (**Figure 31**), the time of first and last motion in the living room is determined, from which the regular bed and waking time can be inferred. On some days, there is a much later time of waking and earlier bed-time. Naturally caution is required in interpretation of such events, as differences may be easily explained by events such as a trip away from home.

**Figure 32** shows the bed occupancy of a patient with pulmonary fibrosis. The black bar shows the time in bed each day. In this example, the patient continued to go to bed around 23:00 each day, but during times of deterioration the patient started to wake earlier, and as severity of the conditioned worsened they had less sleep.

A similar pattern of disturbed sleep was observed in COPD patients during periods of low blood oxygen as indicated in **Figure 33**.

#### **6.2 Technology developments**

Technology for telehealth continues to evolve, improve and be developed. This would include sensors becoming lighter, smaller and the emergence of new types of sensor. Technology continues to improve with microcontrollers becoming more powerful, faster, and use less power, at the same time batteries give greater periods of use. This has made possible an explosion in the availability of wearable devices, particular wrist-worn, that can monitor heart rate, blood oxygen and capture ECG.

Chemistry and biomaterials continue to improve, allowing longer periods of use and providing sensors with greater accuracy in applications such as continuous glucose monitoring.

Wireless technology continues to evolve, with improvements to the range and reductions in power. This allows devices based on technologies such as Bluetooth to operate for longer or use smaller batteries. Likewise improvements in mobile

**Figure 32.** *Daily bed occupancy [45].*

**Figure 33.** *Incidence of disturbed sleep and low blood oxygen [45].*

#### *Results from Telehealth DOI: http://dx.doi.org/10.5772/intechopen.101183*

technology are introducing a range of low-power long range services such as NB-IoT and CAT-M that support direct transmission of data from a device and provide extended battery life.

Telehealth remains beset with devices using proprietary protocols. Although devices may claim to use BlueTooth Low Energy (BLE) as wireless, the data and its format differ significantly between devices and device manufacturers. This locks users into specific devices and applications, which may be acceptable for the consumer market. However it makes it difficult to integrate several devices into a platform to monitor a condition, and many types of device to monitor comorbidities. It also introduces uncertainty to the market regarding continued availability of devices, with consequent loss of investment if a system must be replaced if devices are no longer available.

The situation is not improved by the small number of separate profiles that have been developed for individual medical devices by BlueTooth, and that are fixed in capability. This prevents extensibility and results in proprietary protocols.

There is a great need for protocols that provide semantic interoperability between devices and that are extensible and flexible. The IEEE 11073-20601 [22] base protocol standard and its family of specialisations (IEEE 11073-104xx) with its use of IEEE 11073-10101 nomenclature achieved this goal [66], and is further supported by transparent mapping into enterprise health standards such as IHE PCD-01 and FHIR. However this standard has not been adopted commercially, therefore work has commenced to develop a standard (IEEE 11073-10206) that is based on an abstract version of the object models of IEEE 11073-20601 and that may be used as a guideline to develop other protocols, such as a profile of BLE, that will be semantically interoperable across technologies and thus map transparently to enterprise health standards.
