emPATH Open Sourced Mobile Framework

The emPATH Open Framework:
Supporting Evidence-Based Medicine on Mobile
Devices for Patient Care
Larry Suarez, Jeff Jorgenson, Tom Manley, Melwin Yen, and
Iana Simeonov
mHealth Group
University of California San Francisco, School of Medicine
SuarezL@medsch.ucsf.edu, JorgensonJ@medsch.ucsf.edu,
ManleyT@medsch.ucsf.edu, YenM@medsch.ucsf.edu, iana@calpoison.org
Abstract
Evidence-based Medicine (EBM) is an important endeavor within the medical
community. Unfortunately EBM has been slow to adoption due to a number of factors
including the lack of an infrastructure to construct and apply supporting care pathways
within the busy workflow of a care provider (Evans-Lacko, Jarrett, McCrone, &
Thornicroft, 2010). With the introduction of a major new delivery model in the form of
mobile devices, the question arises if the delivery model can accelerate the application
of EBM for patient care. Care pathways will migrate from the provider's workflow to the
patient's mobile device, alleviating already congested provider workflows. This migration
cannot be ignored because it is already happening with or without the medical
community. There are a large number of undisciplined mobile medical applications
already appearing on the market and reaching patients. But care pathways on mobile
devices may look quite different from care pathways executed by a practitioner. The
UCSF mHealth group in collaboration with University medical researchers has
developed a framework to support the application of EBM on mobile devices. The goal is
to enable mobile devices to be a disruptive new delivery model to help address some of
the major issues confronting the application of patient care through mobile devices.
Disruptive Delivery Model
Mobile devices such as the Apple iPhone provide new ways in which to deliver patient
care. Mobile devices are readily available to patients and are being used to collect
relevant medical data to enhance existing personalized care pathways. But a mobile
device is not merely a data collector. Mobile device features such as cameras, video
conferencing, and on-board sensors will continue to evolve at a tremendous pace.
Mobile device hardware is rivaling laptops in processor speed and data storage size.
Mobile devices are becoming sensor rich already including GPS and accelerometers,
and device-to-device communication will spawn new techniques to support patient
health (Vergados D., 2010). If mobile-based care pathways do not incorporate these
new capabilities then we believe the industry is missing an opportunity to change
medicine. A mobile device will be viewed as a medical "companion" to the patient and as
a medical assistant to the practitioner. Patients can now take control of their treatment

The emPATH Mobile Framework 2
UCSF mHealth Group 11/28/11
and still support close contact with their care provider. A mobile device provides
unmatched visibility into patient health-related events that are otherwise unavailable to
the practitioner. Mobile devices are being used to monitor patients, collect medical
encounters and events, analyze, and identify treatments in real-time (Varshney U.,
2007)(Kotz, Avancha, & Baxi, 2009)(Taylor, & Dajani, 2008), all in conjunction with
existing patient-provider face-to-face encounters. However, a mobile device can also be
viewed as yet another silo in the medical world where static and outdated data and
software is prevalent. Capturing remote data via a mobile device using an out-dated
clinical pathway is useless. The goal is to exploit the extremely real-time nature, unique
to mobile devices in order to realize highly effective real-time EBM solutions.
The mobile application characteristics required for supporting EBM on mobile devices
include:
Dynamic. The care pathways must be able to change in real-time to align with
patient experiences and progress (or lack). In addition, any metrics defined by
the care pathways and which drive data collection on the mobile device must be
dynamic. This area of research is known as "adaptive care management" and
"dynamic care pathways" (Altman, & Altman, 2010).
Personal. Each care pathway on a mobile device must be personalized. The
care pathway must use patient-specific data such as demographics to deliver the
pathway in the most appropriate way. Imagine how distinct a care pathway for a
teen is as compared to an elderly adult. Patients will not follow their care
pathways if the pathways are not personalized.
Relevant. Each patient may be at a different juncture within a particular illness.
If the care pathway does not address the unique issues relating to the stages of
the illness then the care pathway becomes irrelevant to the patient and
potentially dangerous.
Autonomous. Applications must be able to execute with as little human
intervention as necessary to support a clinical pathway. Current market mobile
applications typically require a great deal of human intervention to collect patient
data. Unless a patient can readily see the value of intrusions, they will have little
incentive to follow mobile-based treatment.
Sensor-based. Mobile medical applications will depend more and more on
external sensors or on-board sensors to derive data and hence affect their on-
board care pathways. Care pathways on mobile devices will increasingly depend
on sensors as part of patient treatment (Garg, Kim, Turaga, & Prabhakaran,
2010).
Goal-Driven. Most current market mobile applications are written based on
software requirements defined by care practitioners or software program
managers. However, effective mobile applications should be written based on
patient goals. This is the natural way practitioners manage their patients.
Practitioners typically define goals for their patients, such as increasing mobility,
reducing smoking, and reducing alcohol consumption. By constructing goal-
driven mobile applications, the underlying software can then change the care
pathway when goals change or current patient goals are unmet (Hurley, & Abidi,
2007).

The Connected (and Unrestrained) World
Technology has dramatically changed the way individuals communicate. Individuals
have greater access to information, data, and to one another, and near instantaneous
access to world events via Twitter, Facebook, and news feeds. This is typically referred
to as the "connected world" (Siemens, 2008). Mobile devices have accelerated this
change as individuals use mobile devices as their primary communication medium.
The medical arena has seen a dramatic increase in the use of medical applications on
mobile devices and vendors are rushing to take advantage of this recent euphoria
(Carey, 2011). Patients have access to the latest information concerning care
regiments, medications, support groups, and treatment research. However, the recent
advent of mobile medical applications is clouding the relationship between the patient
and their care providers. Patients may start to rely on mobile medical applications that
are not based on sound medical research and may conflict with current medical
practices, potentially exacerbating hidden patient illnesses such as depression. This
medical mobile application phenomenon is much like the effort by pharmaceuticals to
get between the patient and their care provider using advertisements for their new drugs
(Donohue, Cevasco, & Rosenthal, 2007). By taking advantage of the dynamic
communication that exists in our “connected world”, the mHealth group at UCSF is
creating frameworks and solutions that will help reduce the fracture between patients
and their care providers when using mobile devices and ensure that medical mobile
applications are based on solid research foundations.
Evidence-Based Medicine
Evidence-based Medicine (EBM) is simply defined as the integration of clinical
experiences, clinical expertise, patient values, patient experiences, and the latest best
practice research into the decision making process for patient care (Sackett, Rosenberg,
Gray, Haynes, & Richardson, 1996). The traditional means of describing patient care
processes is through care pathways (Every, Hochman, Becker, Kopecky, & Cannon,
2000). Care pathways consist of steps that either directs the patient in productive ways
(therapeutics) or requests information from the patient for analysis (diagnostics). Care
pathways that support EBM are moving from the clinic to the mobile device. Patients are
essentially carrying their care pathways with them and engaging the pathways as they
interact with their mobile devices. Care pathways become an integral part of a patient’s
life, all day every day, instead of beingrelegated solely to patient-provider face-to-face
encounters. Care pathways allow the practitioner to effectively manage their patients
outside the borders of the clinic. EBM is a continuous process as new information is
integrated into existing patient care pathways. Mobile devices allow care pathways to
take on new dimensions:
Increasingly adaptive. The ability of the care pathway to change in real-time
such as modifying patient medication dosages, adjusting medical devices worn
by the patient, and changing treatment regimes in reaction to illness episodes.
Increasingly personal. Care pathways on a mobile device can conform directly
to the patient. This includes changing how the mobile device interacts with the
patient based on demographics, patient beliefs, patient goals, current patient
health, and care provider goals.

Increasingly aware. Care pathways will incorporate greater amounts of
information from a patient's environment including GPS, data from body-worn
sensors, data from external wireless medical devices, and external data feeds.
This will allow the pathway to adjust treatment and effect patient behavior at the
most opportune time.
The emPATH Framework was designed to anticipate and support next generation care
pathways envisioned for mobile devices. Figure 1.1 shows the envisioned process flow
for supporting EBM on mobile devices.
Figure 1.1: EBM Process Flow
Care providers are integrating national health experiences into their own local
experiences to ensure the best possible outcome for their patients. The Athena Breast
Cancer project, a multi-institution national effort for addressing breast cancer in the
United States, identifies EBM as one of their cornerstone goals (Fernandez, 2009):
"...Use data and risk models to develop personalized, evidence-based
innovations in the diagnosis and treatment of breast cancer."
Technology advances such as sensors are contributing additional relevant information
that directly affects the care pathways that guide patients. For example, prescription
bottles will contain sensors that transmit dosage instructions that will appear within the
patient's mobile care pathways in real-time to ensure proper usage. Figure 1.2 shows
how the patient's environment will affect care pathways.

Figure 1.2: Patient Environment and Care Pathways
The Anatomy of Medical Mobile Applications
Medical applications for mobile devices have a general anatomy (components) which
include:
Engagement. Ability to engage the patient to ensure their participation.
Engagement solutions include gaming interfaces, rewards, personalization,
feedback, and encouragement.
Access. Ability to collect in real-time recent event data either through user input
(for example a survey) or autonomously through on-board or external sensors.
Classify. Ability to classify collected data and any existing on-board data and
determine the correct response based on the current patient care pathways
prescribed by a care provider.
Treatment. Execute the care pathway identified in component 3 (Classify).
UNICEF uses the corresponding process called ACT ("Assess", "Classify", "Treat") in
defining their care pathways for developing countries (UNICEF, 2005). The application
of the individual components may differ within individual mobile solutions but the
components can be readily identified. For example, the popular UCSF mobile application
"Pills vs. Candy" (Carter, 2011) uses gaming to engage the user, a survey to access the
data, a game score to classify the data, and feedback (total score) to treat the user. The
result is an individual that is aware of the issues/dangers concerning the physical
appearance of medications on the market. After taking the survey, an individual is able
to create in their own mind different techniques to help them identify medications.
Each of the components in the general anatomy should be based on medical research.
In other words, EBM can be applied to each component to ensure its effectiveness.
Mobile research at UCSF includes many of the components identified for mobile medical
applications. If the medical application is not engaging, the patient will not continue to
use the application, nullifying any effective EBM treatment. In addition, ineffective

engagement could trigger episodes in other hidden illnesses. If the application is
engaging but the treatment is not effective (not based on EBM) then the patient's efforts
would not be productive.
Mobile Device Care Pathways
The concept of care pathways on mobile devices is very attractive. The EBM care
pathways are intended to be living entities. The emPATH Framework allows care
pathways to exhibit what is referred to as self-star (self*) behavior. Pathways can self-
heal (prune), self-organize (change pathway ordering), self-generate (add new
pathways), and self-optimize (prune redundant pathway steps) (Devaraj, Gupta, Ko,
Thompson, Patel, & Nagda, 2005). The care pathways can change based on the goals
and constraints defined by the care provider or researcher. This dynamic behavior is
required to support a “connected world”. Otherwise the care pathways become silos,
static andstagnant, and eventually harmful to the patient.
The mHealth group and UCSF medical researchers are defining the structure of care
pathways to support self* behavior. Mobile-based care pathways are structured very
much like traditional care pathways but are augmented to support self* behavior. The
combination of a number of technologies are reflected in the care pathways including:
Workflow. Supports the physical requirements of dynamic care pathways. For
example, the ability to prune, augment, and re-order care pathways (Browne,
2005).
Agent-based systems. Supports the analytical requirements of dynamic care
pathways to determine where and when to prune, how to augment a care
pathway, and the most beneficial ordering of a care pathway (Isern, Sanchez, &
Moreno, 2007).
Web 3.0. Controls the internal structure of data within the mobile device, how it
is shared among the on-board software services and pathways, and how that
data is externalized to supporting research systems (Ciccarese, Ocana, Castro,
Das, & Clark, 2010).
The mHealth group uses a common process to construct EBM mobile applications. The
mHealth group has been able to deliver comprehensive mobile medical applications
within days of a researcher's request for an application. Figure 1.3 shows the general
development process. Care pathways are represented in XML (W3W 2008) on the
mobile device. This representation makes it easier to develop the pathway using
standard industry techniques such as the application of graph theory.

Figure 1.3: Care Pathway Generation for Mobile Devices
The emPATH Mobile Framework
The emPATH Mobile Framework provides a platform for researchers and software
developers to deliver dynamic care pathways in support of EBM on mobile devices.
The Framework and supporting care pathways resides entirely on the mobile device.
The Framework can be deployed as a hosted solution but the preferred mode is on-
board the mobile device for reasons including:
Easier for researchers to conduct quick trials without having to be tethered to a
hosted solution.
Supports situations where downtime due to lack of mobile device connectivity
would be detrimental to the patient (e.g., managing depression patients).
Institutions may not have control over the environment of the deployed solution.
The eventual hosting environment may not support chosen vendor hosting
components.
The traditional reasons for using a hosted solution do not readily apply for
medical applications.
Mobile applications for patient care typically require very unique interfaces for
patient engagement hence the rendering subsystem will typically reside on the
mobile device.
The UCSF mHealth group envisions the Framework to be used on small sensor
platforms in addition to its current use on cellular phones. The Framework has been
ported to function on Apple devices (iPhone, iPod, and iPad), Android-based devices,
and J2ME-based devices. Care pathways can be defined using any number of protocols
and representations including XML, RDF (W3W 2004), OWL (W3W 2007), Excel, and
proprietary solutions. Each care pathway is translated by the Framework into a canonical
form for processing.

The emPATH Framework comprises two frameworks: the Core Framework, which
contains features that are necessary to support mobile medical applications and the
EBM Framework, which directly supports dynamic care pathways. Figure 1.4 shows
both frameworks in addition to a Blackboard system (Nute, Potter, Cheng, Dass, &
Glende, 2005).
The Blackboard system supports connectivity between the services of the Core
Framework and the services of the EBM Framework. All services in the emPATH
Framework have access to the Blackboard system and can view real-time changes to
the Blackboard. The Blackboard system acts as a "chalk board" where services can
write items of interest that can trigger other services within the mobile application. For
example, a service monitoring patient temperature could write to the Blackboard that the
patient's temperature has exceeded a threshold. This could trigger care pathways or
other services in the application. The Blackboard is also where the patient's world model
is represented. The world model represents the world as envisioned by the patient
including their biases, their goals, and their current health state. The use of a blackboard
system to "decouple" services/care pathways allows new services and new care
pathways to be added, existing services/care pathways removed, and existing
services/care pathways updated in real-time with minimal impact on the mobile
application as a whole. In agent-based (AI system) terminology, the emPATH
Framework is a stigmergic system.
Figure 1.4: The emPATH Framework
The Core Framework
The Core Framework contains services that are available to all mobile medical
applications. These services are used to generate application features such as the
ability for a patient to interact directly with their care provider. The general architecture is
shown in Figure 1.5.

Figure 1.5: The Core Framework Architecture
The Core Framework contains a number of important features that are necessary to
support medical mobile applications including:
Alert System. The alert system can alert care providers and patients to issues
that require immediate attention. For example, if a patient’s heart rate exceeds a
defined threshold then an alert would be generated and sent to the care provider
and the patient.
Data Integration. Data integration is the ability of mobile applications to
exchange data with existing medical data systems. Data integration is an
important aspect of patient support that may involve multiple care providers.
Research systems can send and receive patient information in any number of
industry data formats such as HL7, Microsoft Excel spreadsheets, XML, and
RDF. The emPATH Framework can also send and receive patient data in any
number of network protocols such as web service calls, SOAP, HTTP, FTP, and
email.
Timers. The Core Framework supports multiple on-board timers to schedule
major patient regimen tasks such as when to take medication, when to create a
diary entry, and when to create a data entry. These timers are necessary to
ensure the patient follows specific care pathways created by their provider. The
Framework will notify the care provider if the patient does not accomplish a
specific scheduled task.
Data capture. The Framework supports data capture from a myriad of medical
devices including anticipated next generation devices. Data capture is one of the
most important aspects of any mobile medical solution.
Patient education. The Framework can provide focused patient information for
disease management, nutrition, and exercise recommendations. Patient
information is the cornerstone of any medical regimen.
Physician-Patient Communication. A care provider can communicate directly
with their patient in the form of “notes” or messages using the Apple Push
Notification system. The patient’s mobile device will vibrate and an indicator

will appear on the phone to tell the patient that a message has been received
from their care provider. Communication is also supported using the standard
mobile device capabilities such as email, text messaging, and cellular.
Remote Data Analysis. An embedded rule system supported by the
Framework can interpret patient monitoring data as it arrives to the mobile
device. The rule system will alert the care provider via email immediately if the
data exceeds normal thresholds or any other criteria defined by the care
provider. New threshold or analysis rules can be uploaded to the patient’s
mobile device in real-time.
Simulation Mode. The Framework can execute in a special “simulation mode”
in which the application will “play” a previous session to allow patients and
physicians to participate in “Human Computer Interaction” (HCI) studies. These
studies do not require medical monitoring devices freeing the participants to
concentrate on HCI issues.
Data Visualization. Ability to graphically display patient monitoring data on the
mobile device.
Real-time Update. Ability to upload to the patient’s mobile device in real-time
new information such as analysis rules, patient background information,
session details, event types, data types, and patient education material. This
can be done without patient participation.
The EBM Framework
The second framework, the EBM Framework, supports dynamic care pathways. The
Framework is designed to spur further research into the application of EBM on mobile
devices by allowing researchers or providers to introduce new components or replace
existing components of the Framework. The interfaces between each component are
well defined by the Framework. The general architecture of the EBM Framework is
shown in Figure 1.6. The red borders indicate well-defined interfaces that allow the
component to be enhanced or replaced. The EBM Framework also supports the
blackboard system. The major components of the EBM Framework includes:
Utterance Engine. The Utterance Engine is the "Watson" (Ferruci et al., 2010)
component of the EBM framework. Its task is to generate utterances
("interactions") to the rendering engine. The interactions (for example, "How are
you feeling today?" or "Please adjust the medical device as follows...") are
determined by on-board care pathways or generated by on-board analytical
software. There are no inherent restrictions as to the complexity of the Utterance
Engine or how the engine derives the utterances.
Rendering Engine. The Rendering Engine is responsible for rendering
utterances to the user. The rendering will differ depending on patient attributes
such as demographics, patient/provider goals, current patient health, and
environmental issues. Current on-going research is deriving the ontology or
language to be used between the Utterance Engine and the Rendering Engine.
For example, the Utterance Engine may use an ontology to request pain
information from the patient without a specific textual utterance. The Rendering
Engine will then render the information request using information from the on-
board PMR and the Blackboard system.

On-board PMR. The EBM Framework supports an on-board Personal Medical
Record system. The system is designed to synchronize with external Electronic
Medical Record (EMR) systems such as EPIC. The on-board PMR is intended to
hold detailed personal health information beyond that of an external EMR
because the mobile device has direct access to the patient and the patient's
environment.
External Sensors. The EBM Framework supports the ability to receive data from
external sensors and to render that data appropriately. For example, a sensor
may be transmitting patient heart rate data in which the EBM Framework will
receive the data and post the information to the Blackboard system for
processing.
Events (Metrics). The EBM Framework supports the ability to store event data
in an encrypted on-board database system. In addition, the Framework allows
researchers to define in real-time which metrics are tracked and managed.
Figure 1.6: The EBM Framework Architecture
Care pathways can reference the blackboard system. For example, if a care pathway
defines a step to request patient information and that information already exists in the
PMR then that step will be skipped. Figure 1.7 shows a reference in the care pathway
XML to an entry in the Blackboard. The ACTIVE element in the XML implies that the
interaction (step) will be rendered only if the patient has cancer in the left breast.

Figure 1.7: The emPATH Framework Blackboard
Conclusion
The emPATH Framework has been used in more than twenty mobile research projects
at UCSF including one NIH RO1 clinical trial. Mobile devices have proved to be an
outstanding delivery model for patient care. With the appropriate care pathways, the
mobile solutions have proved very effective in treating patients. Clinical trial data will
help evolve the Framework as new types of clinical pathways appear for mobile devices.
Current clinical pathways fall short of the potential of mobile devices. Pathways are
typically static in nature and require long cycles to augment. This is understandable
since the validity of a pathway is paramount. But researchers are starting to see the
potential of clinical pathways on mobile devices and are changing their approaches. The
mHealth Group is continually evolving the emPATH Framework to not only keep up with
the needs of the researchers but also to introduce new ways to construct care pathways.
Future Work
The mHealth Group is evolving the emPATH Framework in three ways:
Developing how the Framework interacts with rendering systems including those
providing rich interfaces, gaming systems, and avatar-based systems.
Improving the self* features of care pathways in order to provide more
intelligence in support of autonomous handling of care pathways.
Positioning the Framework to increasingly work with body sensor networks and
body-worn medical devices.

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