**2. Related work**

intake, sleep, exercise, missed meals and stress levels. In practice, most patients struggle with reporting these types of information [5], and these reports are known to be highly inaccurate

These shortcomings present two sets of challenges for technology developers and healthcare providers alike. First, the type, priority, and characteristics of clinically relevant patient data are not well documented. This presents problems for technology developers, who must understand these needs for establishing appropriate technology design requirements. Second, the performance of health tracking devices is not well explored with respect to clinical seizure reporting needs. This presents problems for healthcare providers, who require an understanding of current reporting capabilities for recommending appropriate patient self-

In this chapter, we present our research with clinicians to first establish clinical patient selfreporting needs with respect to clinical decision-making during epilepsy diagnosis and treatment, and second to investigate the extent that the performance of current seizure detection

Our study included a literature review and interviews with clinicians to identify relevant epilepsy-related symptoms and triggers; a card sorting session to prioritize these symptoms and triggers; a technology review of seizure detection devices; and finally, a pair of online surveys was administered for establishing further characteristics of clinical patient self-reporting

Our results include a consensus on the types, priorities, and characteristics of clinical patient self-reporting needs during anti-epileptic drug (AED) selection and treatment along with a comparison of seizure detection devices and patient self-reporting performance. These findings are intended to provide a useful reference for both developing future patient self-reporting tools and to highlight the extent that current technologies may be suitable for addressing the clinical needs that we identified within the practice. Finally, we conclude with a discussion of technology recommendations that could stand to benefit mainstream epilepsy treatment. The main contribution of this work is a "roadmap" for developing technologies that support

**1.** Identification of the **type, priority,** and **characteristics** of self-reported data that clinicians

**2.** Evaluation of current device and patient seizure reporting performance as a reference for

**3.** Identification of **self-reporting challenges** associated with current reporting as a starting

Moreover, our study demonstrated the importance of stakeholder engagement. The role of clinical patient self-reporting is important yet often undocumented in literature. In our findings, we help to address this gap by presenting specific data collection strategies to help

point for developing future patient self-reporting and data collection tools.

and classification devices may be suitable for addressing these needs.

needs during epilepsy diagnosis and treatment, respectively.

need from patients as a reference for technology developers.

epilepsy treatment. The results include:

providers.

[6–10].

70 Seizures

reporting tools.

#### **2.1. Patient self-reporting challenges**

Health information can also be challenging for patients to collect and therefore be unreliable even when available, or it may not be collected frequently enough to be informative [9, 11]. Handwritten and electronic patient "seizure diaries" tends to be either incomplete or inaccurate [5, 12]. In practice, many patients (30–50%) fail to report seizures during the day [6–9] while most patients fail to report seizures at night (85.8%) [9]. Eyewitness accounts often disagree on important details of how a seizure presents [9, 13], and observation is often difficult at night [5, 14]. Reminding patients to fill in reports may be ineffective as consciousness can be impaired both during and following a seizure [9]. These collective challenges present problems for clinicians as important patient information is often unavailable, unreliable, or not collected frequently enough to be informative during diagnosis and treatment [9, 11].

The **types** and **priorities** of patient self-reporting information are therefore important to understand for informing care along with the following self-reporting **characteristics**:


#### **2.2. Health tracking design challenges**

Health tracking technologies could help answer these questions but many practical challenges remain [17]. Health tracking priorities among clinicians and the patient's role in selfreporting are each often under-specified in the literature. There is considerable interest in behavioral surveillance [21] as input for both assessing chronic conditions and evaluating self-management during treatment [22].

*3.1.1. Investigating self-reporting needs*

*3.1.2. Investigating self-reporting priorities*

cards was shuffled beforehand.

from both piles, as shown in **Figure 1**.

We conduct interviews over a 2-month period. The interviews included one-on-one meetings with one nurse practitioner specializing in pediatric epilepsy at the Children's Healthcare of Atlanta (CHOA), Georgia and two attending specializing in adult epilepsy at Emory University Hospital, Georgia. These meetings highlighted important patient self-reporting

Self-Reporting Technologies for Supporting Epilepsy Treatment

http://dx.doi.org/10.5772/intechopen.70283

73

Next, we conducted a literature review to generate a list of patient symptoms and triggers that clinicians might find as useful feedback during diagnosis and treatment. Inclusion criteria for the symptoms included any health indicators that described a specific aspect of the condition such as duration and quality; while triggers included any factors that were known to impact the likelihood of symptoms such as physical activity, sleep quality [28], and selfmanagement behaviors [29, 30]. The literature review resulted in a list of 48 symptoms and 11

The next step was to establish the clinical priority of these symptoms and triggers during diagnosis and treatment. A one-hour card sorting session was conducted with six pediatric epilepsy care specialists at CHOA. Informed consent was obtained from all participants. The

The card sorting was conducted as follows. First, we printed the list of symptoms and triggers from the literature review on two separate stacks of notecards. The same card sorting exercise was conducted twice, with one stack of cards being sorted in terms of usefulness to prioritize data needs during diagnosis, and the second stack of cards being sorted to prioritize the same data during treatment. Each card contained a single symptom or trigger, and each set of note-

The clinicians were asked to order the notecards in terms of "most-to-least" useful patientreported symptoms and triggers during diagnosis and treatment, respectively. Notecards of equal importance were stacked on top of one another. New note cards were added to both piles if the clinicians believed we had overlooked any important symptoms and triggers from our literature review. Likewise, irrelevant or difficult to understand notecards were discarded

The priority ranking for each card was then computed transcribing the notecards into a threecolumned Excel spreadsheet that contained: (1) symptom/triggers names, (2) notecard positions during diagnosis, and (3) notecard positions during treatment, respectively and then

Priority ranking = diagnosis card index + treatment card index (1)

The spreadsheet was then sorted by our resulting priority ranking column from least to greatest for establishing a list of clinical data indicators that were considered important during both diagnosis and treatment. It should be noted that the exercise could have been accomplished

summing the two sorted card indices for diagnosis and treatment as shown in Eq. (1).

participants included four nurse practitioners and two epileptology attendings.

characteristics that we would later include in our online survey.

triggers that may be of interest during either diagnosis or treatment.

These advancements stand to greatly inform traditional diagnosis and treatment, but current health tracking measurements only touch on a small subset of health indicators that are relevant for patient care. The Chronic Care Model [23] is helpful in describing the role of clinical systems, healthcare communications, and self-management in patient care, but it is not instructive in terms of describing what clinical, self-management, and electronic health record (EHR) information is most important to keep track of for achieving positive long-term outcomes.

Current developments can be leveraged for greatly enhancing the capabilities of the existing systems. For example, inertial-based seizure detection wristbands are increasingly capable of detecting convulsive seizures [24]. Most patients have access to smartphones with increasingly powerful sensing capabilities [9, 10]. Well-designed health tracking [25] and health reporting tools [26] have the potential to greatly reduce the burden placed on patients to collect clinically significant health information [27]. It is, therefore, important for researchers to establish an understanding of clinical information needs and health tracking performance for developing appropriate and effective health tracking tools.
