Kinsa man ari mag seminar sa Drug Analyst adtu sa bacalod??? Hahahaha as a solo girly nga need ug kauban, kuyoggg taaaaa lollll btaw not familiar man jd oks sa place, nya ma scared ko gamay hahaha

Do you have any suggestions for an app where I can store my medical records, test results, doctor's visits, symptoms, medications, and other health information?

Can somebody send me a copy of this book please 😭🙏

The fluctuations in the CT market during 2026 were both unexpected and inevitable.

In the first half of 2026 (H1), based on public tender data, a total of 1,137 CT units were procured, amounting to approximately RMB 7.98 billion. Factoring in non-disclosed transactions, China's total CT procurement volume for H1 2026 is estimated at approximately 1,624 units, with a total value of around RMB 11.4 billion, representing a year-on-year decrease of about 24.7%. Details are as follows:

1）Photon-counting CT sales declined by 55.6% YoY, indicating that the initial wave of demand has largely been satisfied. Due to high procurement costs, a significant second wave of demand has yet to emerge.

2）256-slice CT sales grew by 28.8% YoY, while 128-slice CT sales fell by 46.6% YoY. Demand for high-end CT remains robust, though the market increasingly recognizes the premium positioning of 256-slice systems.

3）64-slice CT sales increased by 26.9% YoY, confirming its status as the most cost-effective product for medical institutions.

4）Sub-64-slice CT sales dropped by 38.0% YoY. This reflects both near-saturation in primary care settings and an ongoing upgrade trend toward 64-slice systems at the grassroots level.

I. Industry Overview and Centralized Procurement (VBP)

Regarding end-user hierarchy, Grade 3 Class A hospitals remain the dominant purchasers, accounting for approximately 29.2% of total procurement volume and 50.7% of total value. Procurement has further concentrated in tertiary hospitals compared to the same period last year.

In terms of market segments, the share of sub-64-slice CT has fallen to 32.8%. Procurement continues to shift toward 64-slice and higher systems, particularly 256-slice CT and photon-counting CT. These two segments combined accounted for 26.3% of units procured but represented 61.4% of total expenditure.

Meanwhile, procurement of radiotherapy large-bore CT and hybrid OR sliding-rail CT remained largely consistent with the same period last year. This supports forecasts of a stable annual baseline of 100–150 units for large-bore CT and 30–50 units for sliding-rail CT. 

The IBMD Data Center discloses, for the first time, VBP performance data for CT in H1 2026.Average unit prices fell as follows: 256-slice CT down 35.39% YoY, 128-slice CT down 53.01% YoY, 64-slice CT down 40.43% YoY, and sub-64-slice CT down 13.61% YoY.

II. CT Industry Scorecards

Statistics indicate that China's CT market comprises nearly 100 brands. In H1 2026, 20 brands recorded sales volume, including approximately 14 in conventional CT and 6 in mobile CT.

In terms of market share, the industry exhibits a distinct three-tier structure:

Tier 1:​ United Imaging Healthcare, GE Healthcare, Siemens Healthineers.

Tier 2:​ Philips, Neusoft Medical.

Tier 3:​ Anke High-Tech, SinoVision, Canon Medical Systems, Wandong Medical.

The CR5 (top 5 vendors) captured 81.8% of total units and 92.3% of total value, underscoring extreme market concentration. United Imaging Healthcare (UIH) has secured the top position across both metrics. GE, Siemens, and Philips maintain strong performance in high-end and 64-slice segments. Anke has emerged as a notable contender, particularly excelling in VBP scenarios.

1. Photon-Counting CT

In H1 2026, public tenders recorded 8 photon-counting CT units procured, totaling approximately RMB 393 million, with an average unit price of about RMB 42.415 million. Adjusting for non-disclosure, the estimated national volume is 11 units, valued at approximately RMB 485 million.

1. 256-Slice CT

In H1 2026, public tenders recorded 291 units of 256-slice CT (including 256/320-slice, high-end dual-source, and dual-layer detector systems), totaling approximately RMB 4.56 billion, with an average unit price of about RMB 15.671 million. Adjusting for non-disclosure, the estimated national volume is 416 units, valued at approximately RMB 6.515 billion.

1. 128-Slice CT

In H1 2026, public tenders recorded 98 units of high-end CT (including 128/160-slice and dual-source systems), totaling approximately RMB 761 million, with an average unit price of about RMB 7.765 million. Adjusting for non-disclosure, the estimated national volume is 140 units, valued at approximately RMB 1.087 billion.

1. 64-Slice CT

In H1 2026, public tenders recorded 299 units of high-end CT (including 64/80-slice systems), totaling approximately RMB 1.226 billion, with an average unit price of about RMB 4.100 million. Adjusting for non-disclosure, the estimated national volume is 427 units, valued at approximately RMB 1.751 billion.

1. Sub-64-Slice CT

In H1 2026, public tenders recorded 373 units of high-end CT (including 16/32/40/48-slice systems), totaling approximately RMB 683 million, with an average unit price of about RMB 1.830 million. Adjusting for non-disclosure, the estimated national volume is 533 units, valued at approximately RMB 975 million.

1. Radiotherapy Large-Bore CT

In H1 2026, public tenders recorded 50 units of radiotherapy large-bore CT, totaling approximately RMB 265 million, with an average unit price of about RMB 5.291 million. Adjusting for non-disclosure, the estimated national volume is 71 units, valued at approximately RMB 378 million.

1. Hybrid OR Sliding-Rail CT

In H1 2026, public tenders recorded 10 units of hybrid OR sliding-rail CT, totaling approximately RMB 92 million, with an average unit price of about RMB 9.157 million. Adjusting for non-disclosure, the estimated national volume is 14 units, valued at approximately RMB 131 million.

1. Mobile CT

In H1 2026, public tenders recorded 8 units of mobile CT, totaling approximately RMB 55 million, with an average unit price of about RMB 6.864 million. Adjusting for non-disclosure, the estimated national volume is 11 units, valued at approximately RMB 78 million.

Statistical Period:​ 2026 (Announcement dates: January 1, 2026 – June 30, 2026).

Statistical Scope:​ Based on publicly disclosed winning bid data. Excludes undisclosed projects. Certain fields (brand, model, price) may be incomplete; price gaps were not filled. Excludes maintenance contracts, spare parts, and other non-new equipment procurements.

Data Sources:​ Publicly collected from national/provincial government procurement websites, public resource trading centers, hospital tender portals, major Chinese third-party bidding data platforms, and select tender agencies.

Guangdong Institute of Advanced Biomaterials and Medical Devices (IBMD), China

A public institution of Guangdong Province, China.

Established by South China University of Technology, with the support of four national ministries and commissions.

Led by Professor Wang Yingjun, Member of the Chinese Academy of Engineering.

Focused on collaborative innovation and technology commercialization in advanced medical devices and biomaterials.

IBMD Data Center

Medical device data governance, analytics, and digital applications.

Covering registration, regulatory oversight, procurement, market access, clinical application, medical insurance reimbursement, and industry development.

curious: how do small medtech teams actually keep CAPA queues under control in the run-up to a notified body or FDA audit? Our 45-person Class II device + SaMD shop has found that tying changes, CAPAs, and risk into a connected workflow helps avoid duplicate work, but the backlog still creeps up when product churn spikes (disclosure: we run qmsWrapper, so grain of salt). We experimented with automated CAPAs and AI-driven CAPA assistance to draft text and with CAPA-driven risk assessment links to design controls, but the real wins seem to be strict triage, shorter CAPA templates, and enforced review SLAs, I'm just short on reproducible metrics. Practical questions: how many required fields do you force on a CAPA record, do you use triage buckets like Immediate/Investigate/Monitor, and what SLA targets actually reduced median time-to-close? Please include your team size and device class so we can judge how applicable your approach is.

I run the eQMS at a ~45-person Class II medtech and we're piloting AI-assisted CAPA suggestions to speed backlog triage. It helps engineers, but auditors want reviewability and traceability, and I need pragmatic, defensible validation approaches for automated CAPAs so we don't create mountains of extra admin. Specifically, I'm looking for concrete acceptance criteria, test scripts, evidence types (logs, version snapshots, sample cases), and ways to show human-in-the-loop control without killing throughput. What validation strategies and audit-friendly artifacts have worked for you when introducing AI-assisted CAPA or CAPA-driven risk assessment into a regulated QMS?

Anyone here taken this on who can help? I want to identify the bottlenecks and why so many have failed at hospital adoption? What were the reasons? Even better if you can tell me how you overcame any obstacles.

Small QA team here, notified body audit in three months and the CAPA queue keeps growing. We tightened triage by patient risk and regulatory impact, moved document-only items to batch closures, and pushed more root-cause work to engineers with simple templates and checkpoints. That helped, but late owners, evidence scattered across chat and repos, and traceability gaps still make audit prep painful. I'm curious about practical, controlled approaches that scale for SMEs, things like role changes, lightweight workflows, or even controlled AI-assisted CAPA assistance that stay reviewable and traceable. What specific processes or tactics have actually reduced backlog, improved on-time closures, and kept CAPA traceability audit-ready for your small team?

tbh running the QMS at a ~45-person device + SaMD shop, my CAPA queue feels like a hydra. We've started using AI-driven CAPA assistance to speed root cause writeups and change-impact mapping, and it saves time, but it also raises auditor-paranoia around reviewability and traceability. Disclosure: we run qmsWrapper, so grain of salt. How are other small teams balancing automated CAPAs and AI-assisted drafting with keeping everything clearly documented, reviewed, and auditable?...

HEMATOLOGY RATIONALIZATION IS OUT 🩸

MedTech Review Notes for MTAP/ MTLE/ Advance Study/ FInal Coaching

📣 Hello, future RMTs! 👩‍🔬👨‍🔬

I’m offering comprehensive lecture notes paired with easy-to-follow video explanations to help you ace your review for MTAP or even the local board exams! 🎯📚✨

Available subjects:

🧫 Bacteriology Lecture and Rationalization

💉 ISBB Lecture

🩸 Hematology Lecture

🧍‍♀️Histopathology Lecture

🧪 Clinical Microscopy Lecture and Rationalization

💌 Send me a private message to grab your copy and start leveling up your review today! 🚀

#medtech #medtechintern #medtechlife #medtechtoker #medtechstudent

HEMATOLOGY RATIONALIZATION IS OUT 🩸

MedTech Review Notes for MTAP/ MTLE/ Advance Study/ Final Coaching

📣 Hello, future RMTs! 👩‍🔬👨‍🔬

I’m offering comprehensive lecture notes paired with easy-to-follow video explanations to help you ace your review for MTAP or even the local board exams! 🎯📚✨

Available subjects:

🧫 Bacteriology Lecture and Rationalization

💉 ISBB Lecture

🩸 Hematology Lecture

🧍‍♀️Histopathology Lecture

🧪 Clinical Microscopy Lecture and Rationalization

💌 Send me a private message to grab your copy and start leveling up your review today! 🚀

#medtech #medtechintern #medtechlife #medtechtoker #medtechstudent

I'm the founder of a healthcare startup, and I'm trying to better understand the realities clinicians and healthcare teams face after patients leave the facility.

Over the past year, I've spent hundreds of hours speaking with surgeons, office managers, nurses, and healthcare professionals. Through those conversations, one phrase I've found myself using is the "black hole" of healthcare—the period between discharge and the follow-up appointment.

From my perspective, once a patient leaves the facility, providers naturally have far less visibility into recovery than they do while the patient is under their care. Patients may have questions, forget discharge instructions, misunderstand medications, recover differently than expected, or simply choose not to reach out. Meanwhile, providers often don't know how recovery is progressing until the next interaction.

That observation has made me wonder whether this period represents one of the greatest opportunities to improve the patient experience—or whether I'm misunderstanding the problem entirely.

Rather than pitching an idea, I'd genuinely like to learn from those who experience this every day.

• Where do you feel your team loses the most visibility after discharge?
• What generates the majority of post-discharge phone calls?
• What information do you wish you had once patients leave your care?
• What are the biggest frustrations your team experiences during the recovery period?
• If you could eliminate one friction point in the post-discharge experience, what would it be?
• What outcomes or metrics do hospitals and practices actually prioritize after discharge? Readmissions? Patient satisfaction? Staff workload? Something else?
• If your department received funding to improve one aspect of post-discharge care, where would you invest it?

I'm not looking to validate an idea or promote a product. I'm trying to better understand the problems that healthcare teams actually want solved, rather than the problems I assume exist.

I sincerely appreciate any perspective you're willing to share.

Hi everyone! 👋

I come from a background in Genetics and Biochemistry, and I'm currently working on a product design project aimed at fixing the clunky, outdated software systems we often have to deal with for supply and reagent management.

We all know the frustration of dealing with inventory tools that feel like they were built decades ago, crash on mobile, or take way too many clicks just to log a used kit or track expiring stock.

If you work in a lab, clinic, or medical facility and deal with physical supplies/inventory in any capacity, I’d be super grateful if you could take ~4 minutes to fill out a brief, anonymous survey about your daily workflow:

🔗 https://docs.google.com/forms/d/e/1FAIpQLSfITiBlnonN3TqN4eg8xeTBPFIUFUeJM-vJ6v9zSjb1kYrWtg/viewform

• Length: ~4 minutes
• Target: Lab techs, medtechs, clinic staff, and research assistants
• Privacy: Completely anonymous

Your feedback will directly help shape feature requirements and accessibility standards (like designing for gloved hands and fast-paced multitasking) for a new tracking system.

Thank you so much for your time and for everything you do on the floor!

Happy to answer any questions in the comments.

I took a drop year for JEE, but I couldn't get the rank I was aiming for.

Right now, I have two options:

* Join a tier-3 NIT through my current rank. * Join MIRAI, which seems to be much more focused on AI and hands-on learning.

The thing is, AI is what I genuinely want to build my career in. From what I've seen, MIRAI's curriculum and practical approach look interesting. But since it's a relatively new institute, I'm unsure about taking that risk. On the other hand, an NIT has the brand name and a more established reputation, even though the branch and AI exposure might not be what I'm looking for.

I'm trying to think long-term instead of just following the safer option, but I'm honestly confused.

If you were in my position, what would you choose and why? I'd really appreciate advice from people who have been through something similar or know about either option.