Summary:
The conversation revolves around a proposed Healthcare Leadership Platform that utilizes real-world healthcare data to create realistic scenarios for leadership training. One conversationist aims to develop strategic and ethical leaders capable of governing AI adoption and driving digital transformation in clinical and administrative settings through a PaJR, participatory refined, data driven platform. The discussion also touches on the Digital Personal Data Protection Rules, 2025, and its implications for healthcare data management.
*Key Words:*
- Healthcare Leadership Platform
- Artificial Intelligence (AI)
- Data-driven healthcare
- Digital Personal Data Protection Rules, 2025
- Patient Journey Record (PaJR)
- Data Protection Impact Assessment (DPIA)
- Significant Data Fiduciary (SDF)
Conversational learning Transcripts aka CDSS Conversational decision support and more recently participatory refinement (as in full text here: https://academic.oup.
[13/11, 13:15]hu1: Do they need faculty for this?😊
[13/11, 15:21]hu3: What is the definition of "expert"?
[13/11, 15:23]hu4: This may be ok for Lead with AI in operation. Definitely NOT for Lead with AI in science or technology. 😊
[13/11, 15:33]hu5:
all in all alaguraja.
Tamilians will understand 😀
all rounder in 1 subject
[13/11, 15:40]hu1: May be, an idea brewing for an AI healthcare edtech.
[13/11, 15:51]hu5: bolo
[13/11, 16:49]hu1: Here's a thesis for the esteemed members of the group to review and comment👇
Healthcare Leadership Simulator Platform (The
"MediLead" Platform)
This startup focuses on developing strategic and ethical leaders capable of governing AI adoption, managing complex data streams, and driving digital transformation in clinical and
administrative settings.
Core Concept
A sophisticated, cloud-based simulation platform that utilizes real-world (anonymized and
synthetic) healthcare data to create hyper-realistic scenarios for leadership training.
Target Audience
●
Mid-to-senior level healthcare managers (Hospital VPs, Department Heads, Clinical Directors).
● Future healthcare executives (MBA/MHA students).
● AI/Data Science professionals transitioning into healthcare leadership.
Key Features and EdTech Value
● Dynamic, Data-Backed Scenarios: Leaders tackle challenges like managing a sudden surge in patient volume due to a pandemic (simulating real-time resource allocation), implementing a new diagnostic AI tool while navigating regulatory and ethical concerns,
or managing a system-wide cyber attack on patient data.
● "Digital Twin" Hospital: Participants manage a simulated hospital/clinic "twin" where their decisions (e.g., staffing, technology investment, process changes) have measurable, downstream effects on key performance indicators (KPIs) like patient outcomes, operational efficiency, and staff burnout rates.
● Ethical AI Governance Module: Specific training on establishing AI governance
frameworks, addressing bias in clinical algorithms, ensuring data privacy (HIPAA, GDPR compliance), and communicating AI decisions to clinical staff and patients.
● Personalized Feedback Loop: AI-powered grading and feedback that evaluates not just the outcome of a decision (did the KPI improve?) but the process (was the ethical concern addressed? was communication effective? was the data analysis sound?).
Why this is a good fit?
This moves beyond case studies to experiential learning. It develops the critical decision-making under pressure required for leaders to integrate AI safely and effectively, which is the single biggest technological challenge in healthcare today
[13/11, 16:49]hu1: My AI assistant made it for me😃😃
[13/11, 16:53]hu5: nice disclaimer
[14/11, 13:04]hu1: No takers, no investors?
[14/11, 13:10]hu2: Would love to take it up as hands on experiential learning electives in our campus. More about it here:
Can we get a few potential healthcare leadership trainees to go through this publicly available data generated by our hospital and provide their inputs on how they may have averted this?👇
[14/11, 12:00]hu6: Digital Personal Data Protection Act & Rules published today: https://egazette.gov.
[14/11, 13:12]hu7: Is this a new release sir?
[14/11, 13:13]hu7: Yes - today.
[14/11, 13:14]hu2ai: The **Digital Personal Data Protection Rules, 2025** (DPDP Rules) provide the crucial procedural and operational backbone to the **Digital Personal Data Protection Act, 2023** (DPDPA).
Here is a detailed analysis of the key rules and their direct implications for a **PaJR (Patient Journey Record) workflow**, along with actionable suggestions for compliance.
### Executive Summary
The DPDP Rules significantly formalize and add granularity to the obligations under the DPDPA. For a PaJR system, which handles highly sensitive personal data (health information), the rules around **consent, security, data breach notification, and data retention** are particularly critical. Your workflow must evolve from simply collecting data to actively managing it with demonstrable accountability and robust security.
---
### Key Rule Analysis & Impact on PaJR Workflow
#### 1. Rule 3: Notice by Data Fiduciary (PaJR Provider) to Data Principal (Patient)
* **What it says:** The notice of consent must be clear, standalone, in simple language, and include an itemized description of the personal data and the specific purpose of processing. It must also provide easy means for the patient to withdraw consent, exercise rights, and file complaints.
* **Impact on PaJR:** The generic consent forms you might use now will be insufficient.
* **Suggestions:**
* **Redesign Consent UI:** Create a dedicated, pop-up or page for consent that is not buried within Terms of Service.
* **Granularity:** Instead of "I agree to share my data," specify: "I consent to the processing of my [medical history, medication logs, symptom descriptions] for the purpose of [personalized health tracking, clinician review, generating health insights]."
* **Easy Access:** Provide prominent, easy-to-find buttons/links labeled "Manage Your Consent" or "Withdraw Consent" within the PaJR app/portal.
#### 2. Rule 4: Consent Manager
* **What it says:** Establishes a framework for registered "Consent Managers" – third-party platforms (like account aggregators in finance) that allow users to manage consent across multiple data fiduciaries from a single dashboard.
* **Impact on PaJR:** In the future, patients might use a central health Consent Manager to control access to their PaJR data by hospitals, insurers, or researchers. You may need to integrate with such platforms.
* **Suggestions:**
* **Monitor Ecosystem:** Keep an eye on the development of the Consent Manager ecosystem in India, especially for healthcare.
* **API Strategy:** Plan for a technical architecture that can securely interact with Consent Manager APIs to receive and act on patient consent instructions.
#### 3. Rule 6: Reasonable Security Safeguards
* **What it says:** Mandates specific security measures, including:
* **Encryption, Masking, Tokenization** of personal data.
* Access controls and logging/monitoring of data access.
* Data backup and business continuity plans.
* Retention of logs and personal data for **a minimum of one year**.
* Contractual security obligations on Data Processors (e.g., cloud providers).
* **Impact on PaJR:** This is a direct and non-negotiable requirement. A PaJR system is a high-value target.
* **Suggestions:**
* **Technical Audit:** Conduct a security audit against these specific points. Ensure data is encrypted *at rest* and *in transit*.
* **Access Logs:** Implement detailed logging of *who* accessed *which* patient's record and *when*. This is crucial for breach investigation (Rule 7).
* **Review Contracts:** Ensure your contracts with cloud hosting providers (AWS, Azure, etc.) explicitly assign security responsibilities as required by this rule.
#### 4. Rule 7: Intimation of Personal Data Breach
* **What it says:** In case of a breach, the Data Fiduciary (you) must:
* **Notify each affected patient** without delay, detailing the breach's nature, consequences, and mitigation measures.
* **Notify the Data Protection Board (DPB)** within **72 hours** with a detailed report.
* **Impact on PaJR:** A data breach of health data is a catastrophic event. This rule mandates a swift and transparent response.
* **Suggestions:**
* **Create an Incident Response Plan (IRP):** Have a clear, documented plan that designates a response team, defines communication templates for patients and the Board, and outlines technical containment steps.
* **Practice Drills:** Conduct table-top exercises to test your IRP.
#### 5. Rule 8 & Third Schedule: Data Erasure & Retention
* **What it says:** Specifies that for certain entities (e-commerce, online gaming, social media), data must be erased if the user is inactive for 3 years, unless required by law. More importantly, it mandates that **all data fiduciaries must retain processing logs for at least one year**.
* **Impact on PaJR:** While the 3-year rule may not directly apply to you, the one-year log retention is critical. Furthermore, you must have a clear data retention and deletion policy.
* **Suggestions:**
* **Define a Retention Policy:** Establish a policy stating how long you retain a patient's core health data after account deactivation. This must balance the rule's principle of deletion with other laws (e.g., clinical record-keeping laws that may require longer retention).
* **Automate Deletion:** Build workflows to automatically flag data for anonymization or deletion as per your policy.
* **Log Management:** Implement a system to securely store access and processing logs for at least one year.
#### 6. Rule 10: Verifiable Consent for Children
* **What it says:** Requires obtaining verifiable consent from a parent/guardian before processing a child's data. It specifies methods for verifying the parent's identity and adulthood (e.g., through reliable records or Digital Locker).
* **Impact on PaJR:** If your PaJR is used for pediatric care, you need a robust age-gating and parental consent mechanism.
* **Suggestions:**
* **Age Verification:** Implement an age-collection field during sign-up.
* **Parental Consent Flow:** For users identified as children, create a separate flow that pauses data processing until verifiable parental consent is obtained, using one of the methods outlined (e.g., integration with DigiLocker for verification).
#### 7. Rule 13: Additional Obligations of Significant Data Fiduciary (SDF)
* **What it says:** SDFs have extra duties, including conducting a **Data Protection Impact Assessment (DPIA)** and annual audit, appointing a Data Protection Officer (DPO), and adhering to potential data localization requirements.
* **Impact on PaJR:** Given the sensitive nature of health data, it is highly likely that a PaJR platform will be classified as an SDF.
* **Suggestions:**
* **Proactive Compliance:** Act as if you are an SDF from day one.
* **Conduct a DPIA:** Proactively perform a DPIA for your PaJR system. Identify risks in your data processing and mitigate them.
* **Appoint a DPO:** Designate a Data Protection Officer (even if initially a senior team member) to be the point of contact and oversee compliance.
* **Prepare for Localization:** Be technically prepared to store and process a copy of critical personal data within India if the government mandates it for health data SDFs.
#### 8. Rule 14: Rights of Data Principals
* **What it says:** Data Fiduciaries must publish the means for Data Principals to exercise their rights (access, correction, erasure, grievance redressal) and respond to grievances within a defined timeframe.
* **Impact on PaJR:** Patients will have the right to ask for their data, correct it, or delete their account. You must have a system to handle these requests efficiently.
* **Suggestions:**
* **Create a Rights Portal:** Build a dedicated section in the app/website where patients can easily submit requests to access, correct, or delete their data.
* **Grievance Redressal Mechanism:** Establish a clear process with a defined timeline (the rule suggests a "reasonable period not exceeding ninety days," but best practice is much faster) for responding to all user complaints and rights requests. Publish the contact details of your Grievance Officer.
### Overall Recommendations for Your PaJR Workflow
1. **Gap Analysis:** Map your current data flows, consent mechanisms, security practices, and policies against each rule.
2. **Document Everything:** The mantra of data protection is "Show, don't tell." Document your processes, risk assessments, and decision-making.
3. **Privacy by Design:** Integrate these compliance requirements into the design of new features, rather than bolting them on later.
4. **Team Training:** Ensure your technical, product, and support teams are trained on these rules and their importance.
5. **Seek Legal Counsel:** This analysis is a starting point. Engage with a legal professional specializing in Indian data protection law to review your specific PaJR implementation and ensure full compliance.
By proactively adapting to these rules, you can not only achieve compliance but also build a more trustworthy and resilient PaJR platform, strengthening your relationship with patients and partners.
[15/11, 05:18]hu8: Dear hu2
Interested to see two separate links and a query on how _healthcare leadership trainees_ could help avert the latter.
By itself that term seems to be an oxymoron and also wondering if you want to avert the particular incident which caused a CPA ruling or the adverse ruling. Regrettably the latter has been an unfortunate and sad decision. The death has been caused by a mixture of bad Anaesthesia decision coupled with an emergency. Giving GA to a hypothyroid is fraught with risk. It is not clear but probably Spinal Anaesthesia failed and they had to give GA- The term double Anaesthesia is however a legal invention and making it the basis of the adverse award would means it would be thrown out as a clear evidence of not being commonsensical.
However how your PAJR would have helped prevent this is also not fathomable to me as the adverse circumstances and their interpretation is unlikely to be clear to a lay person and even less so to the so called legal brains.
[15/11, 05:20]hu8: Maybe metaAI could have answered??
[15/11, 08:19]hu2: Thanks for the interesting feedback
🙂🙏
I'll also get the PaJR team to ask AI but for the time being for whatever it's worth here's my human input:
The PaJR workflow is largely about "data driven healthcare."
Patient event outcomes data, drive patient interventions which becomes data to cyclically again influence patient event outcomes data toward creating a positive change in the health of the patient.
The newspaper article where the patient's case report has been shared well enough with some narrative data (not sure if they had a DPDP approved signed informed consent from the patient like PaJR does @hu6 Cnmc or is further debate appropriate around how patient privacy appears to be a convenient trade-off especially when it works positively for either stakeholders in healthcare litigation, more about the misunderstandings around the word litigation forthcoming in another paper) but what a PaJR case report may have offered over and above the newspaper documented data is an hour by hour account of what actually happened during the hours from when this particular patient first felt the symptoms till her death with optimal transparency and accountability.
We have multiple examples that regularly get updated on our home page that could be currently far from the ideal we are striving towards in our daily quest toward using data driven healthcare to improve accountability and avert these misunderstandings because to a physician who has seen many peripartum cardiomyopathies and pulmonary embolisms and has patients regularly dying under his her care this is essentially a pure misunderstanding between those with professional expertise in the subject and between those who have been subjected to that professional expertise.
It's clearly a misunderstanding between those who can see the uncertainty in clinical complexity and those who are sold to the hype of healthcare market forces!
Further reading:
PaJR home page:
[15/11, 08:25]hu6: Privacy in ... is mostly selective.
[15/11, 08:28]hu2: I guess "selective privacy" is a global phenomenon as in"jiski lathi uski privacy!"
[15/11, 09:42]hu8: Agreed, but I quantify further - laws are made and enforced only to benefit the law makers- and here specifically are two examples -PCPNDT and Traffic rules
- though some of the latter do help enforce traffic discipline but PCPNDT has only worsened healthcare
Regarding PAJR and the specific case- I don’t think any non professional saw the Pateint between being wheeled to OR and her death (she was not conscious when she came out)
[15/11, 10:02]hu2: Yes in this specific case all standard professional expertise was delivered and hence the PaJR pitch for making such data available in real time to multiple stakeholders at the point of care even as events unfold before it becomes too late and simply remains newspaper fodder with momentary shock value.
Question is will experts be able to handle a positive Orwellian (non dystopian) data driven healthcare system?
Or will they drown in cognitive overload?
Can @hu1's AI healthcare leadership program make a difference in the current landscape?
As an aside from another parallel discussion around recently viral AIIMS AI usage implications, there's this interesting debate in the comments section of Sumer's post linked here:
Insightful comments from the founder of qure.ai overseeing this AIIMS Radiology triage:
"As a radiologist and a product manager at Qure (full disclosure, the above report appears to be generated by qXR algorithm developed by Qure.ai), my observation has been that sites that are truly successful in capturing the value of implementing AI in their clinical workflow have the following characteristics:
1. They have a clear understanding of why they need automation and AI (Clear need)
2. They have defined expectations from the AI algorithm which match their needs (Defined Expectations)
3. They have clear understanding of the limitations and constraints of AI (Clear Boundaries)
4. Good understanding where AI shines and where Radiologists shine (Collaborative vs Competitive Mindset)
An AI tool is only as successful as the clinical and radiological team that is behind it’s implementation and operation"
Thematic Analysis:*
- *Data-driven Healthcare:* The conversation highlights the importance of leveraging data to improve healthcare outcomes and create a more accountable and transparent healthcare system.
- *AI Governance:* The discussion emphasizes the need for strategic and ethical leadership in governing AI adoption in healthcare, ensuring that AI systems are designed and implemented with accountability, transparency, and patient privacy in mind.
- *Data Protection:* The Digital Personal Data Protection Rules, 2025, are discussed, highlighting the importance of protecting patient data and ensuring compliance with regulatory requirements.
- *Healthcare Leadership:* The conversation touches on the need for effective healthcare leadership, emphasizing the importance of developing leaders who can navigate the complexities of AI-driven healthcare and make informed decisions.
Some key points from the conversation include:
- The importance of creating a culture of transparency and accountability in healthcare, leveraging data to improve patient outcomes and reduce errors.
- The need for healthcare leaders to develop a deep understanding of AI and its applications in healthcare, ensuring that AI systems are designed and implemented to support patient-centered care.
- The importance of protecting patient data and ensuring compliance with regulatory requirements, such as the Digital Personal Data Protection Rules, 2025.
- The potential for AI to improve healthcare outcomes, but also the need for careful consideration of the limitations and constraints of AI in healthcare.
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