{"id":6112,"date":"2026-09-17T17:51:02","date_gmt":"2026-09-17T17:51:02","guid":{"rendered":"https:\/\/pharmanomos.ch\/artificial-intelligence-in-pharmacy-and-qms-opportunities-risks-and-guidelines-for-use\/"},"modified":"2026-09-17T17:58:50","modified_gmt":"2026-09-17T17:58:50","slug":"artificial-intelligence-in-pharmacy-and-qms-opportunities-risks-and-guidelines-for-use","status":"publish","type":"post","link":"https:\/\/pharmanomos.ch\/en\/artificial-intelligence-in-pharmacy-and-qms-opportunities-risks-and-guidelines-for-use\/","title":{"rendered":"Artificial Intelligence in Pharmacy and QMS: Opportunities, Risks, and Guidelines for Use"},"content":{"rendered":"\n

Generative artificial intelligence is rapidly making its way into the operations of pharmacies, pharmaceutical companies, and healthcare organizations: information retrieval, training, translations, draft preparation, preliminary analyses, and administrative support. The potential is real, especially in quality management systems (QMS), where many activities depend on structured documentation, continuous updates, and the ability to quickly retrieve information. <\/p>\n\n

For organizations that are establishing or improving their quality management systems, it may also be helpful to start by examining the principles underlying quality management systems in Swiss pharmacies<\/a>.<\/p>\n\n

But a useful application of AI does not automatically equate to a compliant, reliable, or appropriate one. A language model can produce a response that is very convincing yet inaccurate; it may use outdated information; it may process data sent to a third-party provider; and it may make it difficult to trace how a particular conclusion was reached. <\/p>\n\n

In the healthcare sector, these issues take on particular significance because personal data, health information, professional confidentiality, and professional liability may all be involved in the same process.<\/p>\n\n

That is why the question should not simply be \u201cCan we use artificial intelligence?\u201d<\/strong>, but rather: \u201cFor what tasks, with what data, using what tools, and with what safeguards?\u201d<\/strong><\/p>\n\t\t\t\t

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\n\t\t\t\t\t\t\tArticle Contents<\/strong>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
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  1. AI in Switzerland: What Will the Regulatory Framework Look Like in 2026?<\/a>
  2. Where Artificial Intelligence Can Create Value in the QMS<\/a>
  3. AI Risk-Benefit Matrix in the QMS<\/a>
  4. The risks that a pharmacy or company must manage<\/a>
  5. A Basic AI Policy: A Practical Example<\/a>
  6. From Experimentation to Governance<\/a>
  7. Conclusion<\/a><\/ol>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\n
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    AI in Switzerland: What Will the Regulatory Framework Look Like in 2026?<\/h2>\n\n

    As of September 12, 2026<\/strong>, Switzerland still lacks comprehensive, cross-sector legislation specifically dedicated to artificial intelligence. The Confederation is working on a draft regulation to be submitted for public consultation by the end of 2026. The approach announced by the Federal Council focuses, as much as possible, on sector-specific adjustments and cross-cutting rules centered on areas such as transparency, data protection, non-discrimination, and oversight. <\/p>\n\n

    This means thatthe European Union\u2019s AI Act should not be automatically applied as if it were Swiss law<\/strong>. It may, of course, be relevant to certain cross-border activities, contractual relationships, or organizations that also operate in the European market, but its impact must be assessed on a case-by-case basis. The announced Swiss regulatory approach is distinct and also provides for the implementation of the Council of Europe Convention on Artificial Intelligence. <\/p>\n\n

    However, the absence of a \u201cSwiss AI Act\u201d does not amount to a regulatory vacuum. The IFPDT has clarified that the Federal Data Protection Act (FDPA) is technology-neutral and applies directly to AI-based data processing as well<\/strong>. <\/p>\n\n

    The LPD also considers health-related data to be personal data deserving of special protection<\/strong>. It establishes principles of lawfulness, proportionality, accuracy, privacy by design and by default, and\u2014in cases that may pose a high risk to an individual\u2019s privacy or fundamental rights\u2014requires a data protection impact assessment. It also regulates automated individual decision-making: under certain circumstances, the data subject must be informed and may request that the decision be reviewed by a natural person. <\/p>\n\n

    Professional confidentiality<\/strong> also comes into play for healthcare professions. The FOPH reminds healthcare professionals that they must keep confidential any information received in the course of their work; this includes pharmacists. <\/p>\n\n

    A pharmacist should therefore not view a public chatbot as merely an \u201conline notepad\u201d into which to paste information related to a patient.<\/p>\n\n

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    Where Artificial Intelligence Can Create Value in the QMS<\/h2>\n\n
    <\/div>\n\n

    Document Search and Review of SOPs<\/h3>\n\n

    One of the most promising applications is making internal documentation searchable. An assistant that is linked exclusively to SOPs, instructions, forms, and approved documents can help an employee quickly find the correct procedure or the relevant section. <\/p>\n\n

    The benefit is clear: less time spent searching for files and greater access to corporate knowledge. The key control, however, is to ensure that the system responds based on current documentation<\/strong>, displays the source used, and does not present information as fact if it is not found in approved documents. <\/p>\n\n

    AI can therefore serve as an interface for the QMS, but it should not replace the controlled document. The official source must remain the approved version in the document management system. <\/p>\n\n

    <\/div>\n\n

    Staff Training and User Support<\/h3>\n\n

    AI can transform an SOP into quizzes, training scenarios, FAQs, or summaries tailored to an employee\u2019s role. It can also help users learn how to use software or understand a process. <\/p>\n\n

    This is particularly useful in multilingual organizations: the same training program can be adapted for use in Italian, French, German, and English.<\/p>\n\n

    Here, too, human review is necessary, especially when a translation could alter the technical, clinical, or regulatory meaning of a procedure.<\/p>\n\n

    <\/div>\n\n

    Drafts, documentation, and administrative tasks<\/h3>\n\n

    Minutes, emails, operating instructions, checklists, presentations, process descriptions, and draft documents can be prepared more quickly with the help of an AI assistant.<\/p>\n\n

    For documents subject to the QMS, however, the output should be treated as a draft<\/strong>: review, approval, versioning, and change control remain as defined by the quality system.<\/p>\n\n

    AI can be particularly effective for low-risk administrative tasks, such as reformatting text, summarizing non-confidential content, suggesting a table of contents, or improving linguistic clarity.<\/p>\n\n

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    Complaints, deviations, CAPA, and preliminary data analysis<\/h3>\n\n

    A model can help classify complaints or deviations, identify recurring themes, group similar descriptions, and suggest categories for trend analysis. It can also support brainstorming during a root cause analysis. <\/p>\n\n

    The line that must not be crossed is that of the final decision<\/strong>. The classification proposed by the AI should not automatically determine critical issues, root causes, CAPA, the closure of a deviation, or an impact assessment. <\/p>\n\n

    To explore this issue further, it is helpful to link AI governance to the principles for developing effective CAPA and properly analyzing the root cause<\/a>.<\/p>\n\n

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    Translation and Multilingual Communication<\/h3>\n\n

    In Switzerland, translation is a particularly interesting use case. AI can significantly reduce the time and cost of producing multilingual communications, but texts intended for patients, government agencies, regulated documentation, or operational instructions should be reviewed by someone who is proficient in both the language and the subject matter. <\/p>\n\n

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    AI Risk-Benefit Matrix in the QMS<\/h2>\n\n
    Use Case<\/th>Potential benefit<\/th>Main Risk<\/th>Recommended Minimum Inspection<\/th><\/tr><\/thead>
    Search in the SOPs<\/td>High<\/td>Incorrect answer or outdated version<\/td>Approved documents, visible source, user verification<\/td><\/tr>
    Training<\/td>High<\/td>Simplification or Misinterpretation<\/td>Process Owner Validation<\/td><\/tr>
    Document Drafts<\/td>High<\/td>Errors, fabricated content, poor traceability<\/td>Review and Approval in Accordance with the QMS<\/td><\/tr>
    Complaints and Divertions<\/td>Medium-high<\/td>Incorrect classification or bias<\/td>AI as a support tool; human decision-making<\/td><\/tr>
    Data Analysis<\/td>Medium-high<\/td>Misleading correlations or incomplete data<\/td>Verification of the dataset, method, and results<\/td><\/tr>
    Translations<\/td>High<\/td>Alteration of the technical meaning<\/td>Linguistic and Technical Review<\/td><\/tr>
    Administrative Support<\/td>High<\/td>Disclosure of Confidential Information<\/td>Data Restrictions and Authorized Tools<\/td><\/tr>
    Clinical\/Professional Support<\/td>Potentially high<\/td>Error Affecting the Patient<\/td>Do not delegate professional decisions to the AI<\/td><\/tr><\/tbody><\/table><\/figure>\n\n
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    The risks that a pharmacy or company must manage<\/h2>\n\n

    The first risk ishallucination<\/strong>: the model may invent references, procedures, numbers, or plausible explanations. An AI response should therefore not automatically be considered a reliable source. When the content matters, you must go back to the original document and verify its version and validity. <\/p>\n\n

    The second risk concerns confidentiality and data protection<\/strong>. Entering names, medical histories, prescriptions, contact information, identifiers, or descriptions that could identify a patient into an unauthorized service may result in the processing or disclosure of data that the organization has not adequately assessed. <\/p>\n\n

    The same caution applies to employee data, trade secrets, regulatory documents, and confidential information.<\/p>\n\n

    Cloud-based AI systems also require verification of the provider\u2019s role, any sub-processors, security measures, the location of data processing, and any potential transfers of data abroad. The IFPDT emphasizes that the data controller remains responsible for compliance even when it outsources processing to an external cloud service. <\/p>\n\n

    These issues are directly linked to broader cybersecurity management in the pharmaceutical and healthcare sectors<\/a>.<\/p>\n\n

    A third risk is a failure to validate the system for its intended use<\/strong>. A tool that excels at correcting a sentence is not necessarily suitable for classifying deviations, analyzing trends, or supporting a critical process. The more AI influences an important decision, the greater the need for verification, documentation, control, and monitoring. <\/p>\n\n

    There are also less obvious risks: sudden changes to the model by the provider, service unavailability, variations in results, changes in subcontractors, the use of outdated information, and technological dependence.<\/p>\n\n

    Governance should therefore also encompass the service lifecycle<\/strong> and, for the most critical uses, an exit strategy.<\/p>\n\n

    In the context of drug development and regulatory processes, Swissmedic highlights data quality, model traceability, quality control, and the management of potential biases as critical aspects of AI. These principles are also useful as a governance framework, though they should not be treated as a general rule applicable indiscriminately to every use of AI in pharmacy practice. <\/p>\n\n

    AI Is Being Integrated into Processes: Is Your QMS Ready?<\/h3><\/div>
    <\/div>

    Introducing new tools without first defining data, responsibilities, controls, and procedures can increase risk rather than reduce it. Pharmanomos supports pharmacies and organizations in the pharmaceutical sector in structuring quality systems and processes that are consistent with their operations. Technology creates value when it is integrated into clear, controlled, and documented processes.<\/strong><\/p>

    Discover our services<\/span><\/a><\/div><\/div><\/div><\/div>\n\n
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    A Basic AI Policy: A Practical Example<\/h2>\n\n

    An effective policy doesn’t have to be long. However, it must clearly state what is allowed, what is not allowed, and who is responsible<\/strong>. <\/p>\n\n

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    1. Authorized Tools<\/h3>\n\n

    The organization maintains a list of approved AI tools, specifying the internal owner, authorized purpose, permitted data types, key contractual terms, and the date of the last revision.<\/p>\n\n

    The use of different devices for business purposes should be prohibited unless prior authorization is granted.<\/p>\n\n

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    2. Data That Should Not Be Entered<\/h3>\n\n

    The following should not be inserted into devices not specifically approved for such treatment:<\/p>\n\n