How ALNA Thinks
The sequence begins with what is happening, examines what may be driving it, and leads to an action worth testing. Here is the reasoning behind an ALNA diagnostic process.
1. Understanding the breakdown
Every process starts with a question: what outcome is the organization trying to advance for people using the service? It may be continuity of care, integration, independence, or another meaningful outcome. Against that backdrop, we examine the service pathway and the concrete finding that points to a breakdown, such as wait times, disengagement between stages, workload, or a gap between leadership and frontline perspectives.
A finding describes what is happening. It is not yet an explanation of why, and that's exactly the next step.
2. Testing what may explain it
The same outcome can stem from entirely different mechanisms. ALNA builds several competing mechanism hypotheses, for example workload, unclear ownership, a disorganized handoff between teams, duplicated work, or a skills gap, and examines, for each one, the supporting and conflicting evidence, including what is still unknown and the confidence level in each hypothesis.
This review also looks at working conditions that may be creating unnecessary load, such as duplicated work, unclear ownership, repeated follow-ups, manual tracking, and information that does not move in time. Identifying these conditions makes it possible to consider an action that eases the process without adding another layer of work for the team.
The quality of the evidence also depends on whether staff feel able to report honestly on what is actually happening, a fair organizational culture that allows people to speak up and learn from mistakes without fear. ALNA's measurement happens at the team and process level, and is not intended for individual staff ranking.
This stage is an analytical review. It uses evidence to narrow the possible explanations while keeping uncertainty visible. ALNA does not select an explanation automatically.
3. Choosing what's worth trying
This is where the key distinction happens: ALNA helps shape tailored recommendations based on the possible mechanism, the evidence, and the organization's capacity, and lays out the alternatives and the reasoning behind them before a decision is made. The leading recommendation is explicit. Its rationale, limits, and alternatives remain visible, and the final decision stays with the organization.
Alternatives are reviewed and ranked by:
• Fit with the mechanism
• Strength of the evidence
• Feasibility in the organization
• Additional load on the team
• Resources required
• Time to visible change
• Fit with existing systems
• Risks and unintended effects
• Ability to measure implementation and outcome
• Potential contribution to the person's progress
What the recommendations are based on: action alternatives draw on the information gathered in the organization, the service pathway, the defined breakdown, the mechanism hypotheses and the evidence for each, organizational behavior management (OBM), performance diagnostics in human services (PDC-HS), implementation knowledge, relevant research and case studies, the organization's capacity and resources, and the outcome it is seeking to advance.
ALNA does not copy a solution from another organization. It examines which principles were supported, the conditions in which they worked, what is comparable, and what must be adapted. Research and case studies are reviewed by a person supporting the diagnostic process. They are not treated as an automatic instruction for the organization.
4. Implementing and learning
Once an alternative is chosen, we support its implementation on both the human and technological level, and track a measurable action based on organizational behavior management (OBM) principles: was the change actually implemented, is there an observed change in behavior and process, and what is the near-term outcome for the person. If the outcome hasn't improved, we first check whether the change was actually implemented as planned, an implementation failure is a completely different explanation from a mechanism hypothesis that wasn't supported by evidence, and the distinction between them determines what to do next. These findings feed back and update, sometimes strengthening, sometimes weakening or replacing, the original mechanism hypothesis.
Three layers of time
ALNA helps the organization maintain an updated picture and review changes at agreed points over time. At each decision point, the process examines what may explain the change and learns from what the organization chose, implemented, and achieved:
Ongoing monitoring, "Has something changed that calls for attention?"
Today: a small set of defined metrics and brief updates from the organization. Ahead: connections to existing systems and pattern-based alerts.
Diagnosis at a decision point, "What might explain the change, and what's the evidence for it?"
Data, pulse surveys, documents, and an analytical review, defined together with the organization.
Learning over time, "What was recommended, what was chosen, what was implemented, what changed?"
A repeated check of the mechanism hypothesis in light of what actually happened.
A change in a metric is a signal to look closer, it is not a diagnosis, and it does not prove what caused the change. Today, monitoring is based on sources, metrics, and check-in dates defined together with the organization; there is no continuous automated monitoring or live integrations.
A word on what ALNA isn't
ALNA does not determine the cause with certainty, and does not choose the solution on its own. It raises hypotheses, examines evidence, lays out alternatives, and ranks them. The decision stays with the analyst who supports the process and with the organization.