A Rule-based Content Management Framework for Effective

Development of Intelligent Mobile Apps in Healthcare

Mohammad Raahemi

1 a

, Benjamin Eze

1 b

, Cléo Mavriplis

and Liam Peyton

Faculty of Engineering, University of Ottawa, Ottawa, Canada

Bruyère Academic Family Health Team, Ottawa, Canada

Keywords: Healthcare Applications, Rule-based Content Management, Mobile Applications, Wellness.

Abstract: The number of published healthcare articles is increasing dramatically every year, making it difficult for

physicians and patients to stay current with the latest information related to healthcare. One possible approach

to improving the ability of physicians and patients to stay current with the latest trends in healthcare is through

the use of mobile applications. The challenge to this approach is the lack of a content management framework

that allows medical experts to continuously integrate new knowledge and content into the design of easy to

use software applications for patients and other healthcare personnel. This paper introduces the CANBeWell

mobile application, a rule-based content management application for collecting and aggregating important

medical data from medical experts, and disseminating this data to patients and other clinicians using a context-

aware mobile app in support of preventive healthcare.

1 INTRODUCTION

There is a major push to improve the quality of life

for patients with chronic and complex health

conditions through preventive healthcare (Deek,

2016). With the proliferation of misinformation on

the Internet today, it is becoming increasingly vital

that the content needed for preventive healthcare

comes from the validated sources and provided to

patients in a context that allows them to make

informed and accurate medical decisions (Wald et al.,

2007).

Intelligent mobile applications (apps) have been

developed in a number of areas to inform and help

patients and physicians manage chronic diseases such

as diabetes (Widmer, et al. 2015), inform and support

breast cancer patients (T Ginossar et al, 2017), and

provide app-based interventions for the prevention of

cardiovascular disease (Eng & Lee, 2013).

Also, wellness apps have been shown to increase

self-management in patients and improve outcomes

across various socioeconomic spectrums (Handel,

2011). However, evidence-based information is often

lacking in healthcare apps (Eng & Lee, 2013). Ideally,

medical experts need to be involved in organizing

https://orcid.org/0000-0002-3229-570X

http://orcid.org/0000-0001-8370-0042

reference medical information used in a mobile app,

so it is convenient for patients and providers to use.

But the effectiveness and efficiency of these wellness

apps are always a big concern for medical experts

(Yang & Silverman, 2014). As a result, medical

experts attempt to design and conduct various types

of surveys to resolve such concerns.

This paper proposes a rule-based content

management framework that integrates an easy-to-

use, context-aware mobile application with

preventative healthcare content. Our framework

incorporates an automated workflow for collecting

and aggregating important medical data from medical

experts, data which can then be disseminated to

patients and other clinicians using a context-aware

mobile app in support of preventive healthcare. This

is an early research work being conducted in

partnership with Dr. Cleo Mavriplis of the Bruyere

Research Institute, Faculty of Medicine, University of

Ottawa, with a focus on preventative healthcare

applications (apps).

The rest of this paper is organized as follows.

Section 2 provides some background on rule-based

content management application frameworks and the

usability of mobile applications. Section 3 discusses

522

Raahemi, M., Eze, B., Mavriplis, C. and Peyton, L.

A Rule-based Content Management Framework for Effective Development of Intelligent Mobile Apps in Healthcare.

DOI: 10.5220/0008987405220529

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 522-529

ISBN: 978-989-758-398-8; ISSN: 2184-4305

 2022 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

some related work. Section 4 presents our framework

and CanBeWell application. Section 5 discusses our

findings. Section 6 shows the framework evaluation

based on a survey of the application users and Section

7 provides some conclusions and future work for this

research.

1.1 Motivation

A survey conducted by Paul Krebs and Dustin T

Duncan (Krebs & Duncan, 2015), shows that just

over half of the mobile phone users (934/1604,

58.23%) had downloaded a health-related mobile

app. North Americans are increasingly relying on

mobile technology and the internet for health-related

information and resources (Fox & Duggan, 2012).

The proliferation of smartphone ownership among

US adults, particularly among traditionally

underserved populations (e.g., low-income,

racial/ethnic minorities), has expanded the potential

reach of healthy eating, physical activity, and weight

loss programs. From 2011 to 2015, the percentage of

US adults owning a smartphone increased from 35%

to 68% (Anderson, 2015). In addition, low income

and racial/ethnic minority populations are more likely

to be smartphone-dependent, thus relying primarily

on their phones for health information (Smith, 2015).

In general, the development and adoption of

health and wellness smartphone apps have outpaced

empirical investigations (Boulos et al., 2011).

Making these apps “smarter” through AI can enhance

their accuracy, effectiveness, and efficiency –

increasing users' trust while allowing input from

medical experts, which in turn increases the utility of

these applications.

Benferdia and Zakaria identified all the key user

groups of m-health apps. These groups are patients

(44%), the physicians – health specialists, doctors,

and nurses (37%), general users (13%), and finally

caregivers (7%). Providing the right health

information to the right person at the right time is one

of the most important features of every m-health

application (Benferdia & Zakaria, 2014).

2 BACKGROUND

2.1 Rule-based Content Management

Systems

Rule-based content management systems have been

studied for nearly two decades in application areas

such as geographical information systems (GIS),

metadata catalog systems, and healthcare (Beatty and

Lopez-Benitez, 2012). Based on these application

areas, content management systems can be classified

into two categories - content-type-specific and

general-purpose systems (He et al., 2007).

Content-type-specific systems include 1) video

adaptation scheme for adaptive video streaming

(Chang, Zhong and Kumar, 2001), 2) multimedia

content adaptation scheme such as InfoPyramid that

provides a multimodal, multiresolution

representation hierarchy for multimedia content

(Mohan et al., 2019).

The trade-offs imposed by rule-based systems in

terms of time to deliver content, memory

consumption, and fault tolerance have been analyzed

(Beatty and Lopez-Benitez, 2012). This work

presents a novel approach that uses a rule-based

system to regulate web page generation thereby

improving cache performance and query generation

with large unorganized data sets. Further, there are

approaches that use data mining and statistical

techniques to control content-based rules, such as

associating significant browsing events with specific

contexts (Wang, 2007).

Some general-purpose content adaptation systems

have also been developed, such as BARWAN

(Brewer et al., 1998), and Power Browser

(Buyukkokten et al., 2002). Each of these approaches

has some advantages. However, they do not provide

for flexible and extensible content adaptation. Their

adaptation approach is either fixed or hardcoded in

the system. When the adaptation goal is changed,

such as when a new client device or a new data type

is introduced into the system or a new adaptation

technique is developed, the system must be

redesigned to accommodate the change.

Finally, He et al. (2007) present an extensible

content adaptation system, Xadaptor. This uses a rule-

based approach to facilitate extensible, systematic

and accommodative content adaptation. It integrates

adaptation mechanisms for numerous content sorts

and organizes them into the rule base where rules are

invoked based on the individual client information.

(He et al., 2007)

2.2 Usability of Mobile Apps

Usability is considered important for users to accept

the system. A well-known concept in design and

healthcare systems development, the concept has its

roots in the development of information and

communication systems and human-computer

interaction (HCI) (Larsson, 2013)

Usability is defined as “the extent to which a

product can be used by the specified users to achieve

A Rule-based Content Management Framework for Effective Development of Intelligent Mobile Apps in Healthcare

523

specified goals with effectiveness, efficiency, and

satisfaction in a specified context of use” (Jokela,

Iivari, Matero & Karukka, 2003). Usability is an

important factor in mHealth wellness applications,

especially for people who may find it challenging to

interact with smartphones, PDAs, etc. Besides a user

perspective and an aim toward creating beneficial

solutions that are effective, efficient, and satisfactory,

the context is considered important and has a

significant impact on usability work.

Focusing on usability requires an understanding

of the context before, during and after

implementation (Larsson, 2013). This means that

usability work must be included in all steps of the

development process. Studies showed that hundreds

of thousands of products are returned each year, not

because of its functional behaviour but because of bad

user experience (Kalimullah, 2017)

User experience is a consequence of a user’s

internal state, the characteristics of the designed

system, and the context in which the interaction

occurs. User experience considers the wider

relationship between the product and the user in order

to investigate the individual’s personal experience of

using the product (Kalimullah, 2017). Researchers

and practitioners use different techniques and

methods to capture the customer’s psychological and

behavioural aspects toward a product and have those

incorporated into the design of future products.

2.3 Standards for Usability Testing

Standards and best practices have long been

considered beneficial, as they stop us from designing

based solely on personal opinion or experiences. To

that end, ISO 9241 was defined. This standard is a

multi-part standard from the International

Organization for Standardization covering

ergonomics of human-computer interaction. The ISO

9241-11 standard defines usability as "the extent to

which a product can be used by specified users to

achieve specified goals with effectiveness, efficiency

and satisfaction in a specified context of use"

(International Organization for Standardization,

1998). Poor usability is a major obstacle to health

information adoption (Yen et al., 2012) and a clear

cause of medical errors (Zhang et al., 2003). ISO

9241 identifies three metrics for usability testing:

1) Effectiveness: Measures the accuracy and

completeness towards achieving specified user

goals

2) Efficiency: Measures the resources expended

towards the accuracy and completeness of

achieving set user goals

3) Satisfaction: Measures the level of comfort and

positive attitudes towards the use of a product.

3 RELATED WORK

Similar to our framework, most content management

systems (CMS) used in preventive healthcare

integrate with a clinical decision support system.

There are two broad categories: 1) event-driven CMS

systems - triggering notification based on some

clinical contexts and 2) portal or mobile-based CMS

– use a portal or an app to allow patients to choose the

context for the information they need. Our framework

belongs to the second category.

Gunaratnam et al. (2012) propose a conceptual

framework for a perinatal clinical decision support

system that uses a knowledge-base of rules, a

workflow engine, and a notification engine to help

physicians to send notifications to families on

preventative measures for managing at-risk

pregnancies. The knowledge-base is a content

management system that comes from validated

medial information combined with the patient

electronic medical record required to provide the

context for the notifications sent to the participants.

Cleland et al. (2017) present the usability

evaluation of a mobile app for managing diabetic foot

disease that uses a CMS with thermal imageries,

customized educational content, and game-based

scenarios that help diabetic patients to self-manage

their conditions.

Barbara et al. (2017) describe an evidence-based

database for educating geriatrics-focused public

health professionals. This portal-based CMS provides

reliable health information that informs their

management of care for elder patients. The portal

provides searchable evidence-based content but lacks

annotated graphics or a rules engine for context

customization.

Kuo & Fuh (2011) proposes a clinical decision

support system that uses rules to interpret health

examination results. This work supports a custom rule

syntax for implementing computer-interpretable logic

for interpreting health examinations. These rules rely

heavily on ontologies such as diagnosis code,

procedure codes, rankings, etc., and a reasoning

engine that employs a rule combining algorithm for

decision making. The challenge with this system is

that it lacks a graphical user interface for managing

the rules. That means changes to the rules would

require software changes which is a gap for most

medical experts.

HEALTHINF 2020 - 13th International Conference on Health Informatics

524

4 RULE-BASED CONTENT

MANAGEMENT APPLICATION

4.1 Framework Overview

Our rule-based content management application

leverages declarative rules developed by medical

experts with information on the common health

challenges that affect various organs of the body for

different ages and gender.

As shown in Figure 2, rules are created and

managed by medical experts based on research and

findings from the literature. These rules are then

converted to a machine-readable JSON file and

pushed to a mobile app that is made available to users

using very interactive, mobile-friendly user

interfaces.

Figure 2: The Generalized Model of Rule-Based Content

Management.

As users interact with the application, logs are

collected and used to feed both an analytics

dashboard and machine learning algorithms that also

feed analysis results to the report dashboard. Usage

data and analytics results are fed back to experts to

help provide more content to patients and service

providers.

4.2 Managing Rule-base Content

Rule-based contents are then used to dynamically

display the correct content based on the language of

the user, gender and age of the patient, and whether

they want information appropriate for a patient or a

provider. Since most medical experts are familiar

with using spreadsheets, we decided to use an Excel

spreadsheet to organize and classify information for

these experts. Figure 3 shows a snipped of the Excel

spreadsheet that was used with our rule-based

application. Each of the columns corresponds to the

classification of content. Each row is a rule that says

what content to display, depending on the value of

gender and the age range.

Figure 3: Content Rules Spreadsheet for Medical Experts.

The content in the first row of the spreadsheet is

for a male patient between 40 and 74 years old. The

content in the second row is for a female patient

between 50 and 74. The content in the third row is for

patients of all gender, between 30 and 74 years.

The simple guidelines for the medical expert to

follow are summarized in a single simple

documentation web page that they can refer to. We

have defined two simple annotations that the medical

expert can use. One is to include links with an

appropriate text anchor for supplementary materials.

The other is to embed images in the content

displayed.

The content from the Excel spreadsheet from the

experts is then translated into different languages as

needed to support the targeted users. These files are

then converted to machine-readable JSON files. The

choice of a JSON file is because of its flexibility with

various web and mobile application development

platforms. Regression tests are run on the app to

ensure that it is functioning properly. And finally, the

medical expert manually tests the app to be sure the

content specified is displaying correctly.

To support French and English users, there are

four content files for the application. One file,

topics_en.json, for content on proactive wellness

topics and one file, tests_en.json, for content on the

screening test. There are French versions of those

files as well (topics_fr.json, tests_fr.json).

A Rule-based Content Management Framework for Effective Development of Intelligent Mobile Apps in Healthcare

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4.3 CANBeWell Application

The CANBeWell application was developed by

engineering students in the Biomedical Engineering

Smartphone Training program at the University of

Ottawa in collaboration with Dr. Cleo Mavraplis who

is the medical expert. It is intended for use by both

patients and primary care providers.

CANBeWell organizes information on proactive

wellness topics and recommended screening tests by

age and gender in an easy to use interface that

includes an annotated anatomical image where the

user can click on different parts of the body. Users

on different parts of the annotated anatomical body

image, text and important links with supporting

evidence and supplementary information are

displayed based on the context of the use – provider

or patient.

Figure 4: CANBeWell Application Banner.

Figure 4 shows the banner of the application.

Information is presented to the users visually through

the Body tab or by alphabetically sorted topics if they

click on the Topic tab. The top left corner displays the

current configuration. The content displayed in

Figure 2 is relevant to a female who is aged 52. If the

user clicks in that top left corner configuration panel,

they can change the configuration so that content is

displayed for a different user context and patient. For

example, to a physician, or a patient of different ages

(18-149) and different gender (female, male or all).

In figures 4 and 5, one can see parts of the body

map (In this case for a female). Individual body parts

can be clicked on. In Figure 3, the heart has been

clicked on and is highlighted but one can see and click

on other body parts (lungs, liver, kidneys). There are

also additional icons. A needle for vaccinations is

shown in Figure 5 A sun icon for sun exposure, a

stethoscope icon for physical exams, and a brain icon

for memory problems in Figure 4.

In figure 6, the content available is displayed.

Clicking on the Heart Icon brings up the Heart

Disease topic, and there are three subjects for that

topic: Be Active, Get Tested for Cholesterol, Get your

Heart Risk Score. The specific content that is

displayed depends on the age and gender specified in

Figure 2, and whether the information is intended for

a patient or a physician.

Figure 5: Annotated Body Image with associated icons.

Figure 6: Context-based popup displays information by

subject for a given topic.

Figure 7: JSON document specifying content-based rules

for the application.

Links to evidence or supplementary information

are highlighted. When clicked on, they bring up a

browser to display the evidence or supplementary

information. CANBeWell has built-in multi-lingual

support and is currently available in both English and

French. The content is classified based on Button

clicked, Topic heading, Subject, Patient test, Provider

test, Gender and Age range (Minimum, Maximum).

The topics or tests from the appropriate JSON file

(based on language) are filtered based on patient vs

provider, gender and age. When using the Body tab,

the list is further filtered to only include those

corresponding to the button clicked.

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The .json files (figure 7) used in the CANBeWell

app are generated from the Excel Spreadsheet. First,

macros are used to scan the file and check for any

formatting errors, missing values, and incorrect

values (gender or age out of range, incorrect button

label, etc.).

5 DISCUSSION

CANBeWell app has gone through several iterations

of testing, including two third-party assessments by

healthcare app usability experts. It is now undergoing

a research trial with 30 patients and 10 providers,

which will provide quantitative and qualitative

feedback on usability being carefully monitored as

they use the application.

Enumerated below are some of the strong features

of the application.

▪ Bilingual support (currently supports English and

French).

▪ Optimized for smartphones but runs on any device

(laptop, tablet, smart TV, cell phones) with a

browser.

▪ Body image interface.

▪ Separately and optimally worded text for both

patients and providers.

▪ Easy Filtering and Navigation (body parts, age,

gender, health topics, common tests, patient vs

provider text)

▪ Use of embedded images and supporting links in

the content.

▪ Additionally, the current integration of the app has

support for content management by the medical

expert.

The first iteration of the application was hardcoded as

a native android app. The content was communicated

to developers in documents, but they were

responsible for manually coding the content into the

application.

This approach gave great flexibility in being

creative in how to display the content, but it was not

practical since changes to the content requires code

changes and redeployment of the application.

Especially, since evaluations by usability experts

indicated that consistency and predictability in how

content was displayed in the application were better

overall in terms of usability for the end-user.

The latest iteration of the app is based on the

operational model depicted in figure 8. In this

iteration, the rule-based content framework was

completed. This new version achieves the desired

consistency and flexibility as required by the medical

experts. Most importantly, occasional errors in the

spreadsheet maintained by the medical expert are

easily identified and fixed. Changes were easily made

by simply editing the spreadsheet and regenerating

the .json files.

Figure 8: The operational model.

6 EVALUATION

CanBeWell was evaluated in an Urban Academic

Family Practice in Ottawa through a usability survey.

The survey is adapted from the System Usability

Scale (SUS) and administered as an online

questionnaire to 22 respondents (16 patients and 6

providers). The respondents were allowed a period of

1 month to use the app before they are allowed to

complete the usability survey.

These respondents range from 28 to 64 years old,

with average and median ages of 45 and 40

respectively. The mean age for patients and providers

was 46.6 (25-70) and 32.5 (27-46) respectively. All

the respondents used smartphones – 46% iPhones and

54% android phones. A survey was administered to

these users on the usability of the application.

Answers were scaled from Strongly Disagree to

Strongly Agree (5 Steps).

As shown in Figure 9, the responses show that the

biggest strengths of the app centres around ease of

use. The respondents are confident that they can use

the app, they found it simple to use and follow, and

would even recommend it to their families and

friends. The biggest negative is that most respondents

would not use the app often. They also didn’t feel

enthused with recommending the app to their

healthcare provider.

A Rule-based Content Management Framework for Effective Development of Intelligent Mobile Apps in Healthcare

527

While we couldn’t be very conclusive on these

results because of the small number of respondents, it

points to some areas of improvement with the depth

of content that should be made available to the users.

With more content, users are more likely to use the

app more frequently and with increased use, it would

start coming up at discussions with their healthcare

providers.

Figure 9: User survey responses analysis.

7 CONCLUSIONS AND FUTURE

WORK

It is important to provide researchers and healthcare

experts a platform for managing content in a

systematic manner using tools they are familiar with.

Equally vital is the ability to update the healthcare app

without requiring developer intervention.

The CANBeWell app has the potential to greatly

improve the ability of patients and physicians to stay

current with the latest medical advice on wellness.

The app is easy to use in both English and French and

provides personalized content based on the age and

gender of the user and whether they prefer content

designed for patients or for physicians. More

importantly, the approach to content management is

practical and sustainable allowing the medical

expert(s) to manage the content on their own.

In the future, we plan to include a number of

improvements. We would like to provide the medical

expert with an easy to use HTML interface that allows

the medical expert to use the exact same rule-based

content management that is used now, but with an

interface that can provide immediate rule and content

verification, and optimized support during data entry.

It would also store the content directly into JSON

format so that the effects of the changes can be seen.

We would also like to instrument the application

with clickstream logging and IP address logging. This

will allow us to collect and analyze statistics to see

which content is being accessed, how often, by whom

based on geography, age, and gender.

It would also be useful to have a utility that

constantly monitored all the links to supporting

evidence embedded in the content. A report could be

sent to the medical expert(s) any time a link broke

(supporting evidence no longer available) or the

content referred to by the link changed (update to the

supporting evidence).

Finally, it would be advantageous to link this

development with a news service that monitored and

classified new developments in wellness research and

organized them as a service to the medical expert(s)

maintaining the application.

ACKNOWLEDGEMENTS

This research was funded by an NSERC Discovery

grant and an NSERC CREATE grant.

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