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Disease Information: Making It Relevant To Patients


There is a common look among newly diagnosed patients, the wide-eyed stare of information overload.  Like deer in headlights, it is all too often associated with a paralyzing freeze, resulting in a missed opportunity to engage and inform patients.  This is the first in a series of articles exploring ways to connect patients to personally relevant disease information and educational materials.  This article targets the efforts of healthcare providers to categorize these materials.  A practical guide is presented along with examples using an epilepsy population.


The Great Flood

An internet search on the majority of diseases typically returns tens of thousands of hits.  Although the content within this flood of information is often in-depth and extensive, patients encounter numerous challenges navigating these waters. Some of the main factors include:

Vetting Knowledge

  • Considerable knowledge is often required to appropriately vet and cull available information.


  • The number of search results for most major disease is in the tens-of-thousands.

Information Islands

  • Information is spread over multiple, unrelated websites resulting having to sort through a lot of repetition and different ways information is organized.

Media Type

  • Information can be delivered in a variety of ways (e.g., websites, podcasts, videos, books, etc.), and often publishers will duplicate information across types.

Disease Variation

  • Disease characteristics (e.g., systems involved, stage, progression rate, etc.) and which are the most salient to a specific patient can vary widely.

Domains Impacted

  • Disease states can impact multiple domains (e.g., symptoms, function [activities of daily living]) to varying degrees and relevance to patient's well-being.

Treatment Options

  • Disease states are often associated with considerable variation in treatment options, risks (e.g., side-effects), optimal management, etc.

Stakeholder Interests

  • The target of information stretches beyond the patient, to include family members, caregivers, social networks, etc.

Developmental Characteristics

  • The utility and delivery of information to patients can vary widely based on patient characteristics. Some of the main concerns are developmental stage, education and reading level.

These factors can make gathering disease information an overwhelming and frustrating process for patients, causing many to avoid or cut-short their efforts. This scenario, often repeated across many disease states, represents a missed opportunity by healthcare providers to engage and inform patients, and shape appropriate behavior.

Multiple components are involved in a comprehensive approach to information dissemination, but it starts on the provider-side with a well-organized and categorized toolkit of health-related information and educational materials.  Below are some practical guidelines for categorizing such information using an epilepsy population as an example, which can be modified and adapted for other disease populations.    


As most homeowners know, "Scoop-and-Pour" is often the best way to get control over flooding, so too providers need a mechanism to scoop up the flood of disease information into conceptual buckets. The largest buckets are those found in models of health and wellbeing (e.g., here and here).  Many of the buckets (i.e., domains) are similar across models (e.g., Emotional and Social Health), but there are important differences. Thus, from the provider's perspective, "Scoop-and-Pour" refers to both selecting the most appropriate model, as well as modifying and adapting the buckets (e.g., size, shape, buckets within the buckets, etc.) to best fit the disease population of interest and project aims. A discussion of this process is beyond the scope of this article. 

Instead, this article will assume a model and modifications are in place and focus on practical tips for cataloging the disease information as it is gathered and begin a discussion about ways to trigger information delivery to patients (e.g., manual vs. automated, patient vs. provider driven).  A modified model from the Epilepsy Foundation was used (see here) largely due to the domains being generally applicable to a variety of disease states.  Briefly, the domains are:

Emotional Health

  • Feelings, mood, and related behavior

Stress Management

  • Learning to respond to life’s stressors


  • Restful period of relative inactivity of mind and body

Diet and Nutrition

  • What one eats and drinks

Physical Activity

  • Voluntary body movement and exercise

Independent Living

  • Self-met day-to-day needs capacity

Social Relationships

  • Climate of exchange and interactions between people (social, physical, and verbal)

Education and Employment

  • Continued learning and work-related activities

Cognitive Function (*added)

  • Mental processes involved in thinking or carrying out tasks

Seizure Information (*added)

  • Physical and/or diagnostic characteristics of a disease

General (*added)

  • Disease centered community involvement and events

Treatment and Procedures (*added)

  • Information about various treatments and procedures

Bucket Metadata

Once a model is in place and the search for disease information is set in motion, the process involves using the buckets to classify the information and systematically describing its characteristics. The later can be thought of as assigning "metadata" to the information gathered. Metadata is not intended to be consumed by patients, but rather describes attributes about the information and is primarily used to organize how information is displayed and to match together patients with information that maximizes the likelihood of being consumed and comprehended. The metadata attributes commonly found to be helpful are listed below (see link to spreadsheet catalog template):

Download XLSX • 27KB


  • Description: Identifies the media type

  • Options: Website, Video, Podcast, Text

  • Notes: “Text” refers to written information that will be directly displayed to the patient, as opposed to redirecting the patient via a link (assuming a web-based interface for information delivery).


  • Description: Model domain area

  • Options: Specified by model used. For this example: Emotional Health, Stress Management, Sleep, Diet and Nutrition, Physical Activity, Independent living, Social Relationships, Education and Employment, Disease Information, General, Treatment and Procedures

  • Notes Separate domains into as many sub-domains as needed.

Disease Relationship

  • Description: Identifies whether related to disease of interest (e.g., seizure type) or generally applicable across disease populations (e.g., sleep hygiene)

  • Options: General, Disease Specific

  • Notes: This attribute typically relates to the overall organization and display of information.


  • Description: Identifies the target audience

  • Options: General Public, Scientific

  • Notes: Used to identify whether the informational item targets the scientific community (e.g., a journal article) or the general public. Writing style and assumptions of background knowledge are what drives the need for this distinction.

Reading Level

  • Description: Reading level of any text-based information

  • Options: 5th Grade or below, 6th to 9th Grade, 10th Grade or Above

  • Notes: None

Developmental Level

  • Description: Identifies the appropriate target developmental level

  • Options: Lower School, Middle School, High School or Above

  • Notes: None

Access Information

  • Description: Identifies how accessed

  • Options: URL, File Path

  • Notes: None

Target Person(s)

  • Description: Identifies target person

  • Options: Patient, Caregiver with Adult Patient, Caregiver with Child Patient

  • Notes: None.

Access Link or File Path

  • Description: The link or file path of the item

  • Options: None

  • Notes: If applicable.


  • Description: Item Description

  • Options: None

  • Notes: Summary sentence or two about the information or how it might be used.

Hey Bartender

Although there is a need for ongoing review and search for new or updated materials, the resulting collection of categorized disease information and associated metadata has tremendous potential to engage and inform patients! Given the model's organizational utility and vetting process during the information gathering process, providers may be tempted to simply push everything to patients at once, separated out by domain. This may work in cases where there is a small amount of information (e.g., fewer than 20 items) and/or patients are highly compliant or motivated to consume the information. More commonly, however, the push-everything approach increases the risk of overwhelming and de-motivating patients.

Like a bartender managing a crowd, the alternative approach is to control the flood of information by serving one drink at a time. The best recipes involve matching metadata attributes with patient/disease characteristics and consideration of three key components:


  • The depth and breadth of the information.


  • When is the best time to send the information? Although this sometimes refers to time and calendar-based issues related to maximizing response rates such as day of week, time of day, etc., here "timing" refers to matching information to disease progression.


  • The extent to which the information is pertinent to a specific patient and its level of importance.

Note that what ties these three components together are triggers that signal the release of information. Triggers can range from being manually set by staff (see form below) to being based on the results from automated calculations (e.g., 20% change in seizure frequency rate over a 3-month period).

In summary, the importance of healthcare providers to provide well matched, vetted, timely and relevant information is central to the care of patients. Generating a disease information resource and related metadata attributes is the initial step and it is hoped this article provide guidance in this regards. Subsequent articles will discuss the development of trigger algorithms to maximize the likelihood disease information will be consumed and, ideally, have a beneficial impact on outcomes.

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