Information Overload and Health Decision-Making (Part 3)

This is the third post focusing on the issue of information overload. I previously discussed what information overload is and how it affects us. I will now focus on ways to avoid information overload without restricting one’s ability to gain new knowledge and understanding.

Following are several methods for minimizing information overload:

• Filtering. This involves defining what is useful (e.g., relevant and valid) and what isn’t, and then allowing only the useful information to be accessed. There are many different ways to filter information using software applications, which may include active or passive methods, and personal or social methods (including subject matter experts). See, for example, Collaborative Filtering, Information Filtering, and Intelligent Agent Filtering.


• “Just-In-Time” (JIT) delivery. This involves delivering information in a “just-in-time” (JIT) manner, i.e., having the particular information you need “served to you” when you need, rather than having to search for it.


• Competency-based instruction. This involves tailoring the level of instruction to one’s ability to learn. Imagine an e-learning (distance learning) system that keeps track of your knowledge level about a particular topic (domain) in the curriculum using tests to evaluate what you’ve learned after receiving instruction. You do not receive instruction on subsequent topics until you’ve learned the preliminary information you need to know. And it makes sure you recognize what you still need to learn for a particular situation.


• Personalized presentation. This involves presenting information in a manner tailored to a person’s preferences, i.e., customizing the way information is shown to minimize confusion and maximize clarity, and for maximum ease-of-use.


• Using summary/aggregated data with “slicing, dicing and drill-down” capabilities. This involves combining lots of data into a few aggregate summaries and statistical analyses that give a bird's-eye view,” identify patterns and make predictions, test for statistical significance, and enables people to examine the data from different perspectives, as well as to see the data in “finer levels of granularity” (i.e., view the underlying details). OLAP (On-Line Analytical Processing) tools and spreadsheet pivot tables are technologies that do this through data mining. It is also common to “digital dashboards.”


• Increase your level of knowledge and understanding. While the methods above rely on technology to avoid information overload, strengthening your mind by increasing what you know and understand about a topic/domain enables you to absorb (assimilate) more information in that area without becoming overloaded.

Here's an example of how these six methods can work together to help a healthcare practitioner become more knowledgeable and make better decisions without suffering information overload. Similar things can be done to benefit patients, payers, and others.

Imagine a person with a complex health problem being seen by his practitioner. A computerized diagnostic assessment tool such as the Problem Knowledge Couplers software is used to obtain comprehensive information from the patient and practitioner. It then analyzes all the patient information, matches it with an extensive healthcare database, and presents specific recommendation concerning diagnosis and treatment, with links to relevant studies and other supporting documentation, thereby focusing attention on what’s most important (via information filtering). This information, along with any other relevant patient data stored in the practitioner’s EMR/EHR (electronic medical record/health record) and the patient’s PHR (personal health record), is then display in a patient profile tailored to his particular preferences (via personalized presentation).

Once an appropriate diagnosis and treatment approach are identified, a computerized clinical guidelines system is used to recommend particular evidence-based interventions, including specific protocols to follow. Upon the practitioner’s approval, the system uses this information to generate a targeted plan of care. If the practitioner needs instruction to assist in the delivery of the selected treatment regimen, the system determines what s/he has already leaned (via competency-based instructional methods) and what s/he needs to learn now in order to deliver quality care; it then serves him/her the additional information (via JIT delivery).

When the episode of care is completed and clinical outcomes data are collected, other software application analyzes all the data and presents summary data showing how well the patient responded to treatment compared to very similar patients (via a digital dashboard) on key measures.

By having these outcomes data de-identified and sent to a central data warehouse for research and analysis, they contribute to an evolving base of clinical information that increases knowledge and understanding, thereby enabling the assimilation of even more information, resulting in ever-improving guidelines and decision support processes.