Tentative Hold Expiration: An Outcome Study for Appointment Capacity

Evidence question and study frame

Evidence question: What happens to tentative appointment holds at expiration, and how often does released capacity become a completed booking? The population should be bounded before extraction, and the unit of analysis is one tentative hold with creation time, reason, owner, promised response time, expiration, release, replacement offer, and final disposition. This framing prevents a dashboard total from mixing requests, appointments, messages, and client outcomes. It also forces the team to state when observation begins and ends. Eligibility rules should include the appointment types, channels, locations, and statuses in scope. Exclusions should be recorded rather than silently removed. A pilot sample is useful for discovering ambiguous fields before the full observation period. The protocol should name who can access records, how identifiers are protected, and how corrections are logged. This study is an operational measurement design. It is not a clinical trial, a forecast, or proof that one scheduling practice causes an outcome.

Evidence synthesis and relevance

Evidence synthesis: Open access scheduling evidence provides context for the relationship between access design and outpatient outcomes. The broad no-show review cautions that attendance findings vary by setting. Electronic notification meta-analysis and Cochrane reminder evidence support recording whether a client received and could respond to a time-sensitive message before interpreting expiration outcomes. These sources answer different questions and should not be pooled into one invented benchmark. Official workflow and occupational sources describe work, roles, or safety practices. Reviews and trials summarize defined populations and interventions. For this question, they justify careful definitions, direct workflow observation, and explicit communication exposure. They do not provide a universal target for a local calendar. The local evidence should therefore report counts, denominators, time windows, and missingness next to every percentage. Readers should be able to distinguish a measured event from an interpretation and an interpretation from a recommended action.

Methodology and reproducible measurement

Methodology: Follow all tentative holds created during a fixed enrollment period through a longer outcome window. Separate confirmed before expiration, extended with reason, released, expired without review, and duplicate holds. For released slots, record whether another client accepted and attended. Stratify by lead time and service type. Preserve the raw event state before recoding so another reviewer can reproduce classifications. Use a written data dictionary with examples for common and borderline cases. Store not observed separately from no, because an absent timestamp is not evidence that an event did not happen. Before analysis, inspect duplicate identifiers, impossible time order, and records that cross the observation boundary. Report channel, service type, location, lead time, and day pattern when sample size permits. Pair the numeric summary with a structured review of a small set of typical and unusual records. That record review can explain process mechanisms without pretending the examples represent the whole population.

Limitations and interpretation boundaries

Limitations: A released slot that remains open may reflect low demand rather than a poor rule. Attendance cannot be attributed to expiration alone. Message delivery records do not prove comprehension, and hold reasons may be entered inconsistently. Short studies may miss seasonal demand and rare appointment types. Selection bias can enter when only visible failures are recorded or when staff change behavior during observation. Documentation quality may improve during the study, producing an apparent increase in events even when operations are safer. Confounding remains likely because staffing, demand, season, service mix, and technology can change together. Small subgroups should be shown as counts rather than unstable percentages. Missing data should be reported by field and group. The analysis cannot establish causation without a stronger comparison design. Any local action should be tested prospectively with the same definitions instead of treating an initial descriptive study as a permanent truth.

Conclusion and next evidence

Conclusion: Choose expiration windows by lead time, decision dependency, and communication evidence. Monitor extensions and duplicate holds separately. The aim is fair, visible capacity while preserving a reasonable opportunity for the original client to decide. Publish the result with a concise protocol, inclusion rules, denominator, observation dates, and versioned data dictionary. Separate supported findings from operational hypotheses. A finding describes what the records show. A hypothesis proposes why the pattern exists. An action specifies what will change, who owns it, and when it will be reassessed. This separation keeps the research distinct from a general scheduling guide. It also lets leaders revise a workflow without rewriting past evidence. The strongest next step is usually a bounded pilot that changes one control while preserving measurement. If the pattern does not repeat, retain the result as context rather than forcing a broad policy.

Research methodology

Methodology summary. Follow all tentative holds created during a fixed enrollment period through a longer outcome window. Separate confirmed before expiration, extended with reason, released, expired without review, and duplicate holds. For released slots, record whether another client accepted and attended. Stratify by lead time and service type. Analysis must retain the numerator, denominator, observation window, exclusions, missing values, and version of every classification rule.

Data sources and methodology

This brief reports published findings as stated by each source. It does not combine study populations into a new benchmark; local operators should treat the figures as context and measure their own workflow.

1. Dantas et al., Open Access Scheduling Review: Systematic review of open access scheduling and outpatient outcomes.
2. Dantas et al., No-shows in appointment scheduling: Systematic review of factors and variation in missed appointments.
3. Guy et al., Digital Notifications and Clinic Attendance: Systematic review and meta-analysis of electronic notifications.
4. Gurol-Urganci et al., Mobile Messaging Reminders: Cochrane review of mobile phone messaging reminders.

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• Research library
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Research interpretation questions