Third Next Available (TNA) - A Tool for Patient Scheduling?

Third Next Available, abbreviated as TNA or 3NA, is a calculation commonly used in medical practices to measure patient accessibility, or provider availability. The idea is to determine, at a specific point in time, the third available appointment slot for a specific appointment type. This represents a realistic time patients will have to wait on average to get an appointment with that provider for that specific appointment type (New Patient, Follow-up, Annual Wellness Visits, etc.). The reason to use the third next available and not the first next available is that the first next may have become available a few moments ago due to a cancellation or reschedule, so third next give a reasonable margin of error in calculating provider availability. The concept is to reduce the 3NA value to as low as possible, depending on the specialty, in order to improve patient accessibility to an optimum level.

The concept of appointment availability introduces a possibility of schedulers to think that this is an effective tool to use for scheduling. However, one must not forget that as soon as the first appointment is booked, the 3NA number could change considerably depending upon the new third next available appointment. It is best to use the actual provider schedule within the practice management system for determining the next available appointment. 3NA is a tool to be used for trending by calculating this week after week on the same day/time, and helps only t determine how busy the provider schedule is on an average. Based on this trend, the practice administration can determine if the provider schedule needs to be tweaked, or more resources need to be made available, or add another provider is appropriate. Keep in mind that 3NA makes sense for each appointment type individually, and comparisons between providers of similar specialties provides a better criteria to work off of.

It can, thus, be concluded that 3NA is not an effective tool to be used for patient scheduling, rather it is a measure of provider capacity to see patients in a timely manner or, in other words, patient access to a provider.

Business Intelligence - The Intro

BI Dashboard examples (Click to Enlarge)

Business Intelligence does not have a formal definition but the term is used for a variety of tools and activities used to analyze raw data and present it in a more meaningful and useful format for business analysis purposes.

The term Business Intelligence has been abbreviated to BI and is used alternatively. We will stick to using the abbreviated form BI for representing Business Intelligence in all our Articles, Blogs, Guides and Forums.

BI provides current performance status in relation to past performance and predictive  future views, and provides the target users (business leadership) the ability to modify (slice and dice) and dive deeper (drill down) or summarize (drill up) information. The data becomes even more useful when a comparison to industry specific benchmarks is incorporated.

BI helps organizational leadership to make informed decisions and take calculated risks in both operational and strategic spheres. KPI's can be established and performance can be measured against those. Performance Indicators breaching preset thresholds utilizing easy to read and at-a-glance visuals, can lead to further digging (slicing/dicing and drilling) into the data.



BI information is generally represented as Dashboards, which are collections of related visuals and reports, providing an at-a-glance . Raw data is converted into an OLAP cube. OLAP stands for Online Analytical Processing, which may be defined as a computer based technique for analyzing data and providing insights. Cube is used to define a mutli-dimensional data sets. traditionally a data set has two dimensions , rows and columns. OLAP cubes have more than two dimensions and those with more than three dimension are also sometimes refer to hypercubes. The advantage of cubes is the data is pre-processed and readily available for slicing, dicing and drilling, saving time compared to running queries in real-time against traditional relational data sets.