Signal Detectors

Signal Detectors

Signal detectors were created as programming code to detect motivating performance information as signals in any individual's past performance data. They were tested with clinical performance data that exhibited the idealized features of motivating performance information, such as increasing trends and negative performance comparisons for an individual healthcare professional or team. This code can be understood as an interpretation of theory-based definitions for each type of motivating performance information.

There are currently signal detector functions for 11 classes of motivating performance information, including their subclasses and additional magnitude characteristics that moderate the motivational potential of feedback messages. These functions are undergoing refactoring as standalone knowledge models in PFKB. One example of a signal detector function is the comparison() function, which detects comparison signals against a predefined list of comparators using performance levels in performance content. For this function, the signal is calculated as a simple difference, and it returns a list of resources representing each signal detected, classified as a positive or negative comparison, with its magnitude characteristic, “comparison size”.

Comparison Signal Detector

Concept

The comparison signal detector function compares the performance level of a feedback recipient for a specfic measure against the level of a benchmark, such as the peer performance benchmarks or the goal value for that particular measure. This function evaluates differently for both types of gaps, both positive and negative, relative to the performance level and the list of comparator values that are pre-defined by the pipeline. When the recipient's performance level is above that of a comparator, the comparison _detect method denotes that there exists a positive gap in performance level, which is a kind of motivating information. When this detector is activated, the positive or negative gap motivating information is used to influence the rank of corresponding precision feedback message templates.

Example 1

Below is an example of performance data that leads to the detection of a comparison signal for the measure PONV 05.

Month	Performance Level	Peer Average	75th Percentile Benchmark	90th Percentile Benchmark	MPOG Goal
Oct	85%	85	88	92	90
Nov	85%	85	88	92	90
Dec	91%	85	88	92	90

• For the month of December, this performance data will generate a positive gap signal relative to the 50th and 75th peer performance percentile benchmarks, as well as a negative gap signal between the performance level and the 90th percentile benchmark.

Example 2

Below is an example of performance data that leads to the detection of a comparison signal for the measure TEMP 02.

Month	Performance Level	Peer Average	75th Percentile Benchmark	90th Percentile Benchmark	MPOG Goal
Jan	92%	90	92	95	92
Feb	90%	90	92	95	92
Mar	88%	90	92	95	92

• For the month of March, this performance data will generate a negative gap signal relative to the 50th, 75th and 90th peer performance percentile benchmarks.

Implementation

Trend Signal Detector

Concept

The trend motivating information detector function compares the performance level of a feedback recipient for a specfic measure as it changes over time. The trend detector currently evaluates over a three month window, and detects monotonic and non-monotonic performance trend information. The signal detects when the performance level has a positive or negative slope over time, and extracts the magnitude of teh trend as motivating information to be used as a moderator.

Example 1

Below is an example of performance data that leads to the detection of a trend signal for the measure PONV 05.

Month	Performance Level	Peer Average	75th Percentile Benchmark	90th Percentile Benchmark	MPOG Goal
Oct	85%	85	88	92	90
Nov	85%	85	88	92	90
Dec	91%	85	88	92	90

• For the month of December, this performance data will generate a positive trend signal. The magnitude of the trend will be evaluated as 3, the formula below denotes how this magnitude is determined:
  • trend magnitude = (month t0 - month t2)/2
  • trend magnitude = (91 - 85)/2 = 3

Example 2

Below is an example of performance data that leads to the detection of a trend signal for the measure TEMP 02.

Month	Performance Level	Peer Average	75th Percentile Benchmark	90th Percentile Benchmark	MPOG Goal
Jan	92%	90	92	95	92
Feb	90%	90	92	95	92
Mar	88%	90	92	95	92

• For the month of March, this performance data will generate a negative trend signal. The magnitude of the trend will be evaluated as -2, the formula below denotes how this magnitude is determined:
  • trend magnitude = (month t0 - month t2)/2
  • trend magnitude = (88 - 92)/2 = -2

Implementation

The method operates with the current formula: (Month t0 performance - Month t2 performance) / 2