Augmenting Decision Support at Adaptive Case Management (ACM) Platforms


Traditional ACM(1) augments background BPM(2) decision-support at Cases via two methods : RALB(3), FOMM(4).compass-152121_640

  1. Allowing Users to micro-schedule assigned tasks (RALB).
  2. Allowing Case Managers to periodically assess progress toward meeting Case goals and objectives (FOMM).

Statistical Overlays

Augmented decision support at Cases can be provided via statistical overlays of mined data across completed Cases, at active Cases :

  1. Provisional assignment of durations for not-yet-current tasks.
  2. Engagement of a CPM (Critical Path Method) algorithm that calculates actual/expected dates at the Case end node.

i.e. Practical use of this setup would provide advice/assistance as follows – engage this sub-pathway and get to Case closure in eight (8) weeks, engage another sub-pathway and get to Case closure in six (6) weeks.

Caveat

Traditional CPM assumes a merge of all pathways to a single end node. Cases typically have multiple end nodes.

It follows that unless users are prompted to indicate one or more successor nodes at each Case end node, at the time the node is declared to be “complete”, the CPM algorithm will not be able to calculate the “critical path”.

Probabilistic Branching Overlays

Given that, unlike CPM, ACM engagement of some divergent sub-pathways is optional, data mining can further augment decision support at ACM platform branching decision boxes via probabilistic overlays (i.e. users chose option “A” 40% of the time, users chose option “B” 60% of the time).

Clearly, some filtering is required when data mining (i.e. exclude Cases that did not go to successful closings; exclude branching decision box probabilistic overlay options that have low rates of reported use).

Note that if seasonal filtering is in effect for data mining, a 40/60% overlay for “summer” can easily display/shift to 20/80% or 80/20% for “winter”, depending on the focus of a Case.

Recommendation
If you do not currently have an initiative to improve Decision Making at your ACM platform, my recommendation is to study the potential of statistical overlays and probabilistic overlays before jumping onto the RPA and AI “bandwagons”.

 

Terms used in this Blog Post:

(1) ACM – Adaptive Case Management

(2) RALB – Business Process Management

(3) RALB – Resource Allocation, Leveling and Balancing

(4) FOMM – Figure of Merit Matrices

About kwkeirstead@civerex.com

Management consultant and process control engineer (MSc EE) with a focus on bridging the gap between operations and strategy in the areas of critical infrastructure protection, major crimes case management, healthcare services delivery, and b2b/b2c/b2d transactions. (C) 2010-2019 Karl Walter Keirstead, P. Eng. All rights reserved. The opinions expressed here are those of the author, and are not connected with Jay-Kell Technologies Inc, Civerex Systems Inc. (Canada), Civerex Systems Inc. (USA) or CvX Productions.
This entry was posted in Adaptive Case Management, Decision Making, FOMM, Risk Analysis and tagged . Bookmark the permalink.

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