How AI Is Changing Healthcare Work: Tasks, Trust, and Accountability

The answer: map one task and its handoffs before choosing a move

When AI starts changing healthcare work, map one recurring task from input to handoff before deciding whether to retrain or leave. Separate what a tool can draft or classify from whether your employer has approved it, who checks the result, who acts on it, and who owns correction. The ILO report Generative AI and Jobs: A Refined Global Index of Occupational Exposure supports looking at task-level transformation; it does not supply a job-loss probability for an individual healthcare worker. If a credible current-role redesign is available, test that path against your actual duties. If reductions are active, no redesign route exists, or a required qualification is changing, investigate transition options in parallel rather than waiting for a task map to settle the question. This is a near-term decision guide, not a forecast of whether a profession will disappear.

A useful first map is small: name the task, the information it starts with, the output AI may produce, the person who verifies it, and what happens when it is wrong or incomplete. That makes hidden work visible, including exceptions, judgment, patient-facing communication, and responsibility for the downstream record or decision. It also gives a manager a concrete basis for clarifying approved uses and changed duties. A map cannot protect someone from an announced layoff or replace local employer information, so act on those signals at the same time. The free task checker organizes change-pressure signals; it is not a validated probability of displacement. The paid roadmap compares scenarios and constraints, without guaranteeing employment or income.

Sources: Generative AI and Jobs: A Refined Global Index of Occupational Exposure

What does exposure tell a healthcare worker?

Exposure is an early signal about tasks that technology may be able to affect; it does not tell a healthcare worker that a job will be lost. The ILO report Generative AI and Jobs: A Refined Global Index of Occupational Exposure assesses occupational exposure through task-level analysis and frames transformation of work as more likely for many occupations than full automation. That is a useful boundary for interpreting an exposure label, not a prediction about a particular employer, clinic, or worker. The estimates draw on occupational task descriptions and expert assessment across countries, so they cannot establish how much of one person's week is affected or what will happen in a specific workplace.

Keep the steps separate. Technical capability means a system can perform a bounded operation under some conditions, such as drafting or classifying text. Exposure asks whether parts of an occupation's task bundle are technically susceptible. Adoption requires an employer to acquire, authorize, and integrate a tool; redesign means duties, protocols, staffing, or handoffs actually change around it. Labor demand then reflects many forces, including service needs and replacement hiring, while displacement is a worker or role losing work. Each step depends on conditions the earlier one cannot settle: quality in context, cost, privacy and security controls, workflow fit, available review, and organizational decisions. A capability demonstration therefore cannot stand in for evidence of adoption, and adoption alone cannot establish that headcount or demand will fall.

Healthcare makes these gaps consequential because many tasks connect information work to patient context, coordinated action, and accountable decisions. A draft may be technically plausible yet omit a detail needed by the next person; a classification may need clarification against a source record; an output can be unusable if local protocols do not permit it. This does not mean every task is protected by regulation or that human involvement guarantees safety. It means the worker should ask which exact task is approved, what review or escalation procedure applies, and whether the resulting duties and staffing have changed. Do not infer legal protection or immunity from the fact that a role is healthcare work.

For a personal decision, seek evidence at the later steps in that sequence: a locally approved tool for a named task, revised protocols, training or access requirements, changed review responsibility, and documented changes to duties or staffing. Those observations would strengthen the case that work is being redesigned; a vendor demonstration or general exposure estimate would not. Conversely, repeated use of a tool on routine digital work can materially change how a task is done before aggregate occupation counts reveal any movement. Exposure is worth investigating early, but its proper conclusion is a question about the work to verify next, not a verdict about the occupation.

Sources: Generative AI and Jobs: A Refined Global Index of Occupational Exposure

Where in a healthcare task bundle does AI meet a human handoff?

A practical way to locate where AI might alter healthcare work is to trace one task from its input through a produced output, a check, downstream use and responsibility for correction. The map makes two different workflows visible: medical-record classification or retrieval begins with information already recorded, while clinical documentation begins with an encounter whose meaning may depend on what was said, what was left unsaid and what matters to that patient. The steps that look similar on a diagram can therefore require different kinds of review.

Consider a clearly hypothetical records workflow. A referral or encounter document enters a record system; a tool proposes a category or retrieves a relevant item; a records specialist checks the proposed result against the source and the request; the verified item is placed where the next authorized worker can use it; unclear or conflicting information is corrected or escalated to the designated owner. This is an illustration of how to map handoffs, not a report of an observed clinic or a claim about a particular product. Its useful question is not simply whether classification can be generated, but what makes the result complete enough for its next use.

O*NET’s profile “Medical Records Specialists (O*NET 29-2072.00)” describes work that includes compiling, coding or classifying information, maintaining records, reviewing them for accuracy and completeness, protecting confidentiality, and resolving unclear information. Those descriptions show that the occupation contains both regular information operations and exception-handling duties. They do not report how a particular specialist divides a workweek among these activities, which systems an employer uses, or whether any step is already automated. A task list is a map of possible work components, not a time study or implementation record.

Classification and retrieval may present more repeatable digital inputs when documents follow stable formats, categories are clear and the relevant source is accessible. That makes them reasonable points to investigate for software assistance. But a misfiled item can be consequential precisely because the next user treats its location or label as meaningful. A check must therefore compare the proposed category or retrieved document with the originating record, the purpose of the request and any applicable local rule. Missing identifiers, conflicting entries, unfamiliar document types or ambiguous wording create branches in the workflow. The practical work may shift toward recognizing those branches and resolving them, even if routine cases take fewer manual actions.

Now map a hypothetical documentation workflow: an encounter conversation is the input; an ambient scribe proposes a clinical note; a clinician compares it with the conversation, chart and relevant context; the corrected note may enter the record and inform later care; the clinician or another locally designated role resolves omissions, inaccuracies or concerns. Speech-to-text and drafting can transform conversational material into structured prose, but the encounter is not just a clean file conversion. A statement may be uncertain, a medication name may sound similar to another, an important negative may be omitted, or a patient’s priorities may change how a detail should be represented. Questions and clarifications also involve patient communication, not merely text repair.

The point of the handoff map is to name the human work that remains conditional: finding source context, noticing a mismatch, asking for clarification, deciding whether the note is fit to sign, and knowing what to do when the result cannot be reconciled. Those activities differ from the routine generation step and may sit with different people under different local workflows. The person who corrects a sentence may not be the one who owns a clinical decision; the person who files a record may not be authorized to interpret it. A workflow map should record these divisions instead of assuming that “human in the loop” means one worker performs every check.

A local task map can be made concrete with a few questions at each handoff: what source must be available; what output is proposed; what contextual comparison makes it acceptable; who handles exceptions; where does the corrected information go; and who has authority to stop, amend or escalate it? Also note whether the tool displays its source, preserves an audit trail, and allows correction without duplicating work. These are investigation prompts, not claims that every system provides those capabilities or that every organization assigns the same roles. They help distinguish an apparently automatable operation from the full chain needed to make its result usable.

The distinction matters when estimating exposure inside a role. If stable, high-volume inputs are routed through a validated narrow workflow, some component may be automated end to end. Conversely, exceptions, poor source quality, sensitive context or costly downstream errors may keep review and coordination central. Neither possibility can be inferred from an occupational description or a product demonstration. For the worker, the map points toward evidence to gather locally: which step is actually approved for use, what cases are excluded, whether review duties changed, and whether exception ownership or staffing has been reassigned. That makes the handoff map a way to investigate work design, not a presumption that every task requires a person at every step.

Sources: Medical Records Specialists (O*NET 29-2072.00)

What did the randomized ambient-scribe trial actually measure?

The peer-reviewed NEJM AI article “Ambient AI Scribes in Clinical Practice: A Randomized Trial” supports a narrow conclusion: in one short outpatient study, Nabla was associated with a modest reduction in recorded time-in-note relative to usual care, while the DAX Copilot estimate was not statistically significant. The trial also documented incomplete use and preliminary survey signals. It is evidence worth considering when evaluating a local documentation workflow, but it does not establish that ambient scribes improve patient outcomes, reduce staffing, transfer less work overall, or protect any clinician’s job.

The pragmatic three-group randomized trial assigned 238 outpatient physicians across 14 specialties in one California academic health system to DAX Copilot, Nabla or usual care. The intervention ran from November 4, 2024, to January 3, 2025. Random allocation makes the product comparisons more informative about the outcomes measured in this setting than a demonstration or a satisfaction anecdote would be. It does not make every observed difference universal: the participating physicians, institution, duration and workflow define the population and circumstances to which the results most directly apply.

The primary measure was change in log writing time-in-note, an electronic-record measure of documentation time. For Nabla, the reported comparison with control was a 9.5% reduction (95% confidence interval, 1.8% to 17.2% reduction; P=0.02). For DAX Copilot, the estimated 1.7% reduction was not statistically significant (95% confidence interval ranging from a 9.4% reduction to a 5.9% increase; P=0.66). These are distinct product-specific findings. The DAX result does not demonstrate a reduction, and the Nabla result does not establish that the same change would occur with another tool, another site or a different documentation process.

Use was partial, not universal even within the intervention arms. The article reports DAX use in 33.5% of 24,696 visits and Nabla use in 29.5% of 23,653 visits; about 15% of physicians assigned a scribe never used their assigned tool. The visit percentages describe use across recorded visits, not the share of physicians who adopted a tool or the share of all their documentation labor automated. They show why assignment to a tool and actual use need to be distinguished when interpreting results. They also leave open practical questions about which encounters were selected, why use varied, and what happened during visits without the tool.

The time-in-note measure has a further boundary: it does not include time physicians spent editing drafts inside the vendors’ platforms. A reduction in the measured interval therefore cannot be read as an equal reduction in total documentation labor. Review, corrections, navigation between systems, patient communication and handling exceptions may occur outside the measured time. The trial does not establish that all those activities increased, decreased or stayed constant; it simply did not count all of them within its primary endpoint. For a local evaluation, that omission makes it sensible to observe the whole workflow, including review and editing, rather than treating one time metric as a complete accounting of work.

The study also collected clinician survey measures related to work experience and task load. Some comparisons moved in a favorable direction for scribe groups, suggesting possible improvements, but these were secondary outcomes, based on survey responses rather than the primary time measure. The authors say those secondary-endpoint findings require confirmation in larger multicenter trials. They should therefore be described as promising but uncertain signals, not confirmed improvement in burnout, workload or professional fulfillment. Survey respondents were not a complete substitute for direct measurement of patient care, total labor or longer-term workforce effects.

Accuracy reports belong alongside those possible benefits. Clinicians reported occasional clinically significant inaccuracies on the study’s survey questions and rare bias. Those reports do not provide a complete error rate for every generated note or show that every error reached a signed record. Equally, the short-term workflow finding cannot be treated as proof that review is unnecessary. The practical implication is that evaluation needs to include what kinds of inaccuracies workers encounter, whether source context is available, how corrections are handled and how much time those steps require. These are local questions; this trial did not resolve them for every specialty or institution.

Randomization gives the primary comparisons evidentiary weight, so the results should not be dismissed simply because they are limited. Their value is precisely product-specific and bounded: one tool achieved a statistically significant reduction in a particular recorded measure during a brief trial, while the other did not, under conditions where use was incomplete. Taken together with the outcome’s omitted editing time and the uncertainty around secondary surveys, the article justifies a careful local assessment of both documentation time and the full review burden. It does not justify a claim about patient outcomes, staffing levels, work shifted to other roles, other clinicians’ results or job security.

Sources: Ambient AI Scribes in Clinical Practice: A Randomized Trial; 2 in 3 physicians are using health AI—up 78% from 2023

How does reported use differ from a redesigned workplace?

No. Physician surveys show that reported use has become more common and that doctors have views about the conditions for broader adoption; they do not show that healthcare employers have redesigned jobs. A respondent who tried a public tool for a draft, a clinician using an organization-approved application for a bounded task, and a team working inside a formally integrated workflow may all report “AI use.” Those reports describe different arrangements. The AMA’s survey snapshots are useful evidence about physician experience and stated priorities, but cannot establish which arrangement applies to a particular worker or whether responsibilities, staffing, or job design changed.

In “2 in 3 physicians are using health AI—up 78% from 2023,” the AMA reported that physician self-reported use rose from 38% in 2023 to about two-thirds in 2024. Respondents named uses such as documentation, drafting, translation, research summaries, and assistive diagnosis. This helps identify work categories where some physicians say they are using AI; it is not an independently observed audit of tools or a measure of how often each use occurs. Nor does a physician-only survey establish use among nurses, technicians, administrators, medical-records staff, or healthcare employees as a whole. A reported use may involve a one-off experiment, a personally selected tool, or a system provided by an employer; the survey figure alone does not sort those cases or show that a process was changed around them.

The 2026 AMA report “AMA: AI usage among doctors doubles as confidence in technology grows” surveyed 1,692 physicians and reported 81% professional AI use. It also found that respondents identified data privacy and robust safety and efficacy validation as important to broader adoption, and that many wanted physician consultation or direct involvement. These figures describe professional self-reports and expressed conditions, not measured compliance, system performance, or proof that organizations acted on the preferences. They make governance a relevant subject for a workplace conversation, while leaving the actual policies and practices at any one employer unknown.

For a worker deciding what the reported use means locally, the useful evidence is concrete and attached to a task. Ask which tool and task are approved, what information may be entered under applicable privacy and data-handling rules, and which cases must remain outside the workflow. Then ask whether a written procedure defines review, whether review duties or handoffs have changed, who monitors performance and exceptions, and who owns escalation when the output is incomplete or unsuitable. These are questions derived from the difference between survey reports and operational arrangements; they are not claims that the AMA surveys establish a particular rule. A named tool without a defined task or data policy may indicate access or experimentation, while formal task approval, training, workflow documentation, monitoring, and assigned exception ownership provide stronger evidence of integration. Even those signals describe implementation, not by themselves a change in headcount or a worker’s future employment.

Self-report surveys still matter: they can show that use reaches beyond demonstrations in respondents’ working lives and can reveal which applications and adoption conditions deserve attention. But the worker’s career decision turns on whether their own duties, review load, authority, and staffing have changed. That conclusion requires local workflow evidence; the survey totals cannot supply it.

Sources: 2 in 3 physicians are using health AI—up 78% from 2023; AMA: AI usage among doctors doubles as confidence in technology grows

Why does trust depend on workflow design?

Trust in a clinical AI contribution should mean calibrated reliance on a defined use: knowing what the output is intended to support, what it cannot establish, and what a reviewer can check before acting. Fluency is not a reason to rely on an answer, and a human name at the end of a process does not make review meaningful by itself. The reviewer needs enough source context to compare the output with the record and the patient’s situation, a clear understanding of the task and its limits, sufficient time to assess it, and authority to correct, question, or stop its use.

The Agency for Healthcare Research and Quality’s “Reimagining Healthcare Teams: The Patient-Clinician-AI Triad To Improve Diagnostic Safety” is a practice framework for organizing diagnostic work, not a trial showing that a particular review procedure prevents errors. It emphasizes shared goals, clarity about the roles of patient, clinician, and AI, communication, patient contribution, and clinician attention to safety, validity, and reliability. Applied to work design, that framework suggests a reviewer must be able to see how a proposed output relates to its source and intended purpose. If the system provides only a polished conclusion without the relevant record details or context, a reviewer may have too little basis to distinguish a well-supported result from a plausible but unsuitable one. That is a workflow implication, not a measured finding from the AHRQ framework.

Patient context can change whether an output is usable. A summary or suggested next step may omit a concern the patient raised, conflict with a preference, or fail to reflect uncertainty that emerged in conversation. The patient’s account can clarify or challenge the information available to a model; the clinician then has to reconcile that contribution with the source record, relevant evidence, and purpose of the task. A workflow that gives no time or route for that communication can make the nominal review step too narrow to catch a mismatch that matters to this person. Inference from the AHRQ framework: a meaningful check must leave room for the patient’s contribution when it is relevant, rather than treating the generated text as a self-contained answer.

Role clarity matters because the person who can inspect a source may not have authority to amend the record, make the clinical judgment, or halt downstream use. The workflow therefore needs to connect what the reviewer finds with a practical route to act: correct a draft, request clarification, consult the responsible clinician, or escalate an unresolved concern. If correction is technically possible but no one owns the next step, the system has provided an edit function without a usable safety path. Conversely, assigning a final reviewer without access to provenance, time, or authority transfers responsibility on paper while withholding the conditions needed to exercise it. This is a design judgment drawn from the framework’s emphasis on roles and communication, not a claim that role labels guarantee safe performance.

These conditions support warranted reliance, but they do not establish that the model is valid for the case, that input data are sound, or that institutional controls are adequate. AHRQ’s framework helps explain what people and communication contribute around an AI-supported task; it cannot substitute for evidence about the system and its use. For a worker, the relevant question is whether the assigned check has the context, capacity, and authority to change what happens when an output does not fit—not simply whether a human is present.

Sources: Reimagining Healthcare Teams: The Patient-Clinician-AI Triad To Improve Diagnostic Safety

When can verification itself fail?

Adding a reviewer can create a new failure point when the check is treated as a final gesture instead of work that needs time, information and attention. AHRQ’s “Human-AI Interaction” describes automation bias: under pressure, people may give too much weight to a system’s recommendation, especially when its output appears plausible. In an AI-assisted task, that can produce a chain like this: a draft arrives late in a busy shift; the reviewer has little time to compare it with the source; familiar wording seems reasonable; an omitted detail passes through because the person checks fluency rather than completeness. This is an analytical pathway to test, not a claim that every clinician follows it or that a particular tool has caused such an event. The relevant question is whether the design makes independent checking feasible in the conditions where the task actually occurs.

The same capacity problem can affect alerts. AHRQ’s “Human-AI Interaction” also discusses alert fatigue: repeated, excessive or poorly presented alerts may draw less attention over time. If a workflow sends frequent warnings without making their importance or action clear, a reviewer may stop distinguishing a consequential signal from routine noise. Conversely, an alert that is easy to acknowledge but difficult to investigate can increase interruptions while leaving the underlying uncertainty unresolved. These mechanisms do not establish that AI necessarily adds work; they show why counting a review step is not the same as measuring useful error detection. The review task has its own inputs, judgment demands, time cost and possible misses. Its value depends on whether it catches meaningful problems often enough to justify the capacity it consumes and whether the surrounding workflow lets a worker respond to what they find.

For a local evaluation, trace one recurring review from output to disposition. Can the reviewer reach the relevant source material without searching across inaccessible systems? Is time reserved for the check, or must it compete with uninterrupted care and other assigned duties? Can the worker correct or reject the output, and is there a usable escalation route when the discrepancy cannot be resolved? These are focused tests of review capacity, not a generic list of accountability roles. If the answers are no, adding a nominal human checkpoint may shift responsibility and workload while leaving detection weak. If the answers are yes, that still does not prove the tool is safe or the check effective; it identifies conditions that can be evaluated against actual errors, corrections and time spent. AHRQ’s mechanisms mark risks to investigate, not a universal outcome or workload increase. A useful observation records both successful catches and cases where a reviewer had to defer, seek help or repeat work.

Sources: HTI-1 Final Rule

What accountability rules attach to covered decision support?

The federal HTI-1 Final Rule does not assign the same duty to every person who encounters AI at work. Its transparency and risk-management provisions concern defined predictive decision-support interventions within certified health IT. For covered functionality, the rule establishes transparency criteria and calls for relevant source attributes and risk management and maintenance processes. That bounded scope matters: it should not be described as a general federal rule for every generative AI application, every clinical workflow or every individual clinician. Whether a system or actor falls within a regulatory definition depends on the specific functionality and setting. The rule can therefore inform how covered decision support is documented and managed, but its existence alone does not tell a worker which local task they own or establish that every output has been validated for a particular use.

Operational responsibility is a separate work-design question. As a practical inference, a job description or workflow procedure should make it possible to tell who checks that the input is complete, who reviews the system’s output, who makes or authorizes the consequential decision, who records what action was taken, and who receives an unresolved concern. Those activities may belong to different roles: one worker might confirm source data, another might interpret a recommendation, and a designated clinician might make the decision. Naming each handoff helps reveal whether a person has been assigned a task without the access or authority needed to perform it. This is a way to examine the actual allocation of work, not a claim that HTI-1 requires every institution to use those exact job labels or that it makes each clinician personally responsible for every AI-supported result.

The allocation can also differ by institution, tool and task. A covered predictive intervention may sit inside certified health IT and be managed through organizational processes, while another AI-supported activity may fall outside HTI-1’s defined scope. Within one workplace, validation, review, decision-making, documentation and escalation may be split across clinical, technical and administrative roles. The written policy is a starting point; the worker still needs to know how it operates in practice, including where to route a concern and whether the assigned check fits the available time and authority. Regulatory transparency and risk-management requirements can structure information and governance for covered interventions, but they cannot by themselves guarantee validity in a specific case or supply missing review capacity. Accountability is clearer when the rule’s actual coverage and the workplace’s actual division of duties are examined separately. That distinction helps a worker ask which written process applies to their task before treating a broad policy statement as a personal legal instruction.

Sources: Artificial Intelligence and the Health Workforce

How should evidence change the career comparison?

Use broad evidence to decide what to investigate, then use local evidence to decide what to do. The Bureau of Labor Statistics’ “Healthcare Occupations: Occupational Outlook Handbook” projects faster-than-average growth for U.S. healthcare occupations overall and about 1.9 million annual openings over 2025–2035, including openings from both growth and replacement needs. That is a demand backdrop across a large occupational group, not an AI forecast, a local vacancy count, or evidence that a particular healthcare role will grow. It cannot show whether a specific employer is reducing, redesigning, or expanding a particular task bundle. Treat it as a reason to examine feasible options within healthcare, not as a reason to assume a safe landing in any title.

The OECD report “Artificial Intelligence and the Health Workforce” offers a different kind of signal: 13 of 18 responding medical associations identified digital-literacy gaps as a moderate-to-major obstacle, and all 18 supported more physician education. These are association-level views across countries. They support the judgment that practical digital literacy matters to health-system integration; they do not identify a course that employers prefer, establish a credential requirement for every role, or show that completing a program improves hiring or pay. The useful implication is narrower: learn a skill when it answers a documented gap in the work or in a real target role. A course certificate can document participation, but readiness needs to be shown through work samples, supervised practice, or demonstrated performance against the role’s requirements.

That distinction leads to three pathways to compare. An upgrade stays close to the current role: map a recurring process, learn to evaluate outputs and errors, and take on a bounded implementation, documentation, or quality responsibility if the employer has a real need and a route to assign it. An adjacent move uses existing healthcare knowledge in health information, informatics, implementation, or quality work; investigate these titles before investing, because prerequisites may include specific experience, credentials, systems knowledge, or clinical familiarity. A larger change may mean a substantial qualification or a move outside the current sector. Consider it when the first two paths do not meet the worker’s requirements or lack credible openings, and compare the full transition cost and time with the likely benefit rather than treating a new credential as a guarantee.

For each path, check current local postings and ask about actual internal pathways: which duties recur, which requirements appear across employers, whether the worker already meets them, and whether an internal project, transfer, or supervised assignment exists. Compare the pay range only where employers disclose it or a reliable local source supports it; set a personal salary floor rather than inventing a number from national averages. Include location, commute, schedule, credential prerequisites, training duration and cost, and the practical effect on health and family responsibilities. A role that looks attractive in an occupational projection may still be infeasible if entry requires years of unpaid study, relocation, a schedule the worker cannot manage, or a pay cut below essential expenses.

Choose learning after that comparison identifies a gap. Durable foundations may include data literacy, workflow mapping, evaluating system output against source information, privacy practices, and verification. Their value is strongest when attached to a named task or requirement—for example, a posting repeatedly asks for reporting skills, or an internal team needs someone to document how exceptions are reviewed. Prefer a small, testable learning step with feedback before committing to an expensive degree or broad certificate. A growing sector can contain roles that are shrinking or being redesigned, and an adjacent title may remain out of reach without its required experience. If local postings show no attainable path, or the work fails the worker’s constraints, that evidence can favor a larger change; aggregate projections cannot make the choice on its own.

Sources: Artificial Intelligence and the Health Workforce; Healthcare Occupations: Occupational Outlook Handbook

Which next move fits your actual constraints?

If no reduction is underway and there is a credible redesign route, choose one recurring task and ask for duty clarification or an approved, bounded pilot with a clear review point. If reductions are underway, a qualification deadline is near, or no redesign route exists, investigate transition options in parallel now. Do not begin by buying a broad course or trying to forecast the whole occupation. Pick one evidence-gathering action you can fit around your schedule, finances, health, and family responsibilities: examine a relevant local posting, ask about an internal pathway, or compare one training prerequisite with your current experience. If time is scarce, a single conversation or posting can reveal whether a larger investigation is warranted; if money is tight, establish prerequisites and costs before enrolling. Choose the action that answers the decision you face now, then set a date to revisit the choice when that evidence is available. The free task checker can organize task-level change-pressure signals and first actions; it does not calculate a validated probability that you will lose your job. If you need to compare several feasible scenarios, the paid career roadmap offers a personalized comparison and plan, with no guarantee of employment or income.