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Evaluating the Effectiveness of an AI-Powered Physician Assistant in Improving Patients' and Physicians' Satisfaction in Anaesthesiology Clinics

2026年7月31日 更新者:Dileep Kumar、Aga Khan University

Evaluating the Effectiveness of an AI-Powered Physician Assistant in Improving Patients' and Physicians' Satisfaction in Anaesthesiology Clinics of a Tertiary Care Hospital

This trial aimed to evaluate the effectiveness of an AI-Powered Physician Assistant in improving patients' satisfaction with the quality of care. It also aims to evaluate physicians' satisfaction with the integration of a Physician Assistant into their clinical workflows. The primary research question is

A. What is the effect of an AI-powered Physician Assistant on patients' satisfaction with their quality of care compared to the standard care?

The secondary questions are as follows:

B1. How satisfied are physicians with integrating an AI-powered physician assistant into their daily clinical workflows?

B2. Which factors are significantly associated with patient satisfaction regarding the AI-Powered Physician Assistant?

B3. Is there a statistically significant difference in mean consultation time per patient between those receiving AI-assisted care and those receiving only standard of careonly ?

Participants will be enrolled from pre-operative outpatient clinics, including patients attending clinic for anaesthetic clearance prior to surgery and consultant anaesthetists providing care.

Patients will serve as the unit of randomization and will be assigned to one of two study arms on each clinic day. On each clinic day, the first 8 eligible patients presenting for consultation will be randomly assigned to the intervention group or the control group in a 1:1 ratio. Intervention patients will proceed to a dedicated waiting room for structured digital intake via an AI platform (demographics, symptoms, history, clinical data) and receive AI-assisted care. Control group patients will undergo routine standard care protocols. The consultant physician will evaluate both arms during each clinic session, reviewing physician assistant-generated patient summaries and charts for patients in both the intervention and control groups.

Each patient will be asked to fill out the survey at the end of the consultation with the physician. The consultants will be requested to fill out a survey at the end of the day.

調査の概要

状態

まだ募集していません

研究の種類

介入

入学 (推定)

180

段階

  • 適用できない

連絡先と場所

このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。

研究連絡先

研究連絡先のバックアップ

研究場所

    • Sindh
      • Karachi、Sindh、パキスタン、74800
        • Aga Khan University Hospital
        • コンタクト:
        • コンタクト:
        • 主任研究者:
          • Dileep Kumar

参加基準

研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。

適格基準

就学可能な年齢

  • 大人
  • 高齢者

健康ボランティアの受け入れ

はい

説明

Patients and Patients' Proxies

Inclusion Criteria:

  • Consent to participate
  • Age 18 years and above
  • Initial patients
  • Possession of a phone
  • Read and write Urdu and/or English

Exclusion Criteria:

  • Emergency Care patients
  • Follow-up patients

Physicians:

Inclusion Criteria

  • Attending or Consultant Physician
  • Consent to participate
  • Workflow integrated with AI Assistant

Exclusion Criteria

- Residents and Senior Medical Officers

研究計画

このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。

研究はどのように設計されていますか?

デザインの詳細

  • 主な目的:他の
  • 割り当て:ランダム化
  • 介入モデル:並列代入
  • マスキング:独身

武器と介入

参加者グループ / アーム
介入・治療
実験的:AI-Assisted Care
The patients in this arm will receive AI-assisted care in addition to the standard care. The AI platform will take the patient's history, and then the patient will talk to the consultant as part of the routine flow.
The study participant allocated to the intervention arm will interact with the AI-physician assistant application before they consult with the physician. The application will collect medical history of the patient. This will then be followed by an AI-generated clinical summary, which their physicians will receive before the consultation begins. Physicians will review this summary and ask further questions of patients if required. Any additions and changes in the patient's history will also be made. Physicians will subsequently conduct a physical examination of the patient. After this, the physician will be able to view AI- and guideline-based suggestions for the patient's assessment and pre-operative management. The recommendations can be selected, modified, or not used as per the physician's expertise. All additions within the application can be either typed manually or verbalised via an AI-assisted scribe within the application.
介入なし:Standard Care
The patients in this arm will receive standard care. The standard of care for our study is what is usually applied in Anaesthesiology outpatient clinics, with consultants seeing the patients after the resident.

この研究は何を測定していますか?

主要な結果の測定

結果測定
メジャーの説明
時間枠
Patient Satisfaction and Outpatient Experience Score (Adapted NHS Outpatient Survey - Patient Questionnaire)
時間枠:From enrollment to the end of the study, for each patient, for 8 weeks

Patient satisfaction will be evaluated across two domains: (1) effective utilization of waiting time and (2) receipt of patient-centered care during the outpatient visit.

Evaluated post-consultation using a structured survey adapted from the NHS Outpatient Survey: Section A (Wait Time): Wait duration (A1) and wait time utility (A2). Section B (Doctor Interaction): Patient-centered care, consultation duration, listening, and clarity (B2-B7). Section C (AI Assistant - Intervention Arm Only): Perceived listening, trust, and privacy (C1-C6). Section D (Overall Impression): Respect, dignity, and care- quality (D1-D3).

Ordinal items are assigned numerical scores to calculate a composite mean score and domain sub-scores (range: 1.0-5.0). Higher scores indicate greater satisfaction.

From enrollment to the end of the study, for each patient, for 8 weeks

二次結果の測定

結果測定
メジャーの説明
時間枠
Daily Physician Satisfaction and Workflow Efficiency Score (End-of-Day Physician Survey)
時間枠:From enrollment to the end of the study, for each physician, for 8 weeks

Physician satisfaction and workflow efficiency will be evaluated using an end-of-day survey administered to physicians whose patients were recruited into the study. Evaluated using a two-part structured questionnaire:

Section 1 (Comparative Workflow): Binary comparisons across 5 domains (consultation duration, patient engagement, documentation efficiency, cognitive workload, and focus on patient care), with 3-point follow-up sub-questions for favorable intervention responses.

Section 2 (Intervention Utility): 6 intervention-specific items assessing accuracy/trust, workflow efficiency, workload relief, and overall satisfaction (5-point ordinal scales; 1 = Strongly Disagree, 5 = Strongly Agree).

Daily composite mean scores are calculated from Sections 1 and 2 (range: 1.0-5.0). Higher scores indicate greater satisfaction.

From enrollment to the end of the study, for each physician, for 8 weeks
Total Physician Consultation Duration (Stopwatch Timestamps)
時間枠:From enrollment to the end of the study, for each patient, for 8 weeks

Evaluates total physician consultation duration (in minutes) for each patient during their outpatient clinic encounter.

Consultation time refers to the active time spent by the physician while the patient is inside the consultation room. It encompasses the time taken to inquire about symptoms, conduct physical examinations, prescribe treatments, and provide counseling.

Duration is objectively measured in minutes using manual stopwatch timestamps, initiated the moment the patient enters the consultation room and concluded when the patient departs.

Mean total consultation times will be calculated per patient and compared between the intervention and standard care arms. Lower or more optimized consultation durations reflect enhanced workflow efficiency.

From enrollment to the end of the study, for each patient, for 8 weeks

その他の成果指標

結果測定
メジャーの説明
時間枠
Process flow evaluation outcome - Mean Queuing Time (Timestamp Tracking)
時間枠:From enrollment to the end of the study, for each patient, for 8 weeks

Recorded using objective timestamps across two sequential intervals:

Interval 1: Completion of counter registration to initiation of vital signs measurement.

Interval 2: Completion of vital signs measurement to patient entry into the physician consultation room.

Intervals are combined into a single aggregated total queuing time per patient visit. Lower mean queuing times indicate improved operational workflow efficiency.

From enrollment to the end of the study, for each patient, for 8 weeks

協力者と研究者

ここでは、この調査に関係する人々や組織を見つけることができます。

スポンサー

捜査官

  • 主任研究者:Dileep Kumar、Aga Khan University

研究記録日

これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。

主要日程の研究

研究開始 (推定)

2026年8月30日

一次修了 (推定)

2026年10月30日

研究の完了 (推定)

2026年10月30日

試験登録日

最初に提出

2026年7月28日

QC基準を満たした最初の提出物

2026年7月31日

最初の投稿 (実際)

2026年8月3日

学習記録の更新

投稿された最後の更新 (実際)

2026年8月3日

QC基準を満たした最後の更新が送信されました

2026年7月31日

最終確認日

2026年7月1日

詳しくは

本研究に関する用語

個々の参加者データ (IPD) の計画

個々の参加者データ (IPD) を共有する予定はありますか?

はい

IPD プランの説明

The plan is to publish the study, and informed consent has been obtained from each participant. Each participant's data will be de-identified at the source level for both the application and survey.

IPD 共有アクセス基準

Through Publication Only

IPD 共有サポート情報タイプ

  • SAP
  • CSR

医薬品およびデバイス情報、研究文書

米国FDA規制医薬品の研究

いいえ

米国FDA規制機器製品の研究

いいえ

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