How can a hospital reduce pain and delay in patient Discharge?

Patient discharge is the final process in a patient's stay in the hospital. Instead of being smooth process, often the discharge process creates pain for the patient as well as the hospital. The delay often is unpredictable and can span between 2 hours to a full day depending on how insurance clearance process is handled. The insurer-hospitals’ meeting on 9th of this month (October, 2025) has this process under discussion for the right reasons.
The delay for the hospital, means the room is occupied, instead of being prepared for the next patient. It also means pain and added room rental bill for the patient. Economic Times post claims, the delay is attributed to, mainly,

  • Legacy IT systems
  • Bureaucratic treatment file movement
  • “Avoidable” insurer queries
  • Final bill different from initially approved amount
  • Distrust, inefficient coordination between hospitals, insurers

We also found multiple earlier reports that highlight the pain of discharge process.

Here is a short summary:

# Study (Authors / Location) Setting & Sample Key Metrics (Delay, Time) Main Findings / Causes of Delay
1 Reducing discharge delays: using DMAIC approach in a tertiary care hospital (Sharma et al., Dehradun, Uttarakhand) (IJCMPH) Tertiary-care hospital, North India; 1,000 discharged patients Pre-intervention: discharge summary ~235 ± 78 min; financing clearance ~436 ± 451 min; total ~329 ± 389 min. Post-intervention: summary ~72.6 ± 42 min; clearance ~162.6 ± 95 min; total ~208.1 ± 122.9 min. (IJCMPH) Use of DMAIC (process improvement) reduced delays significantly. Bottlenecks: financing clearance, summary preparation.
2 A cross‑sectional study on delay in discharge in a tertiary care hospital in the Malwa region of Punjab (Sharma et al., Bathinda, Punjab) (CiteDrive) Tertiary hospital; n = 250 patients ~80% of participants faced delay; ~40% spent more than 5 h to complete discharge. Average time nearly double the standard set by National Accreditation Board for Hospitals & Healthcare Providers (NABH) standards. (CiteDrive) Delays at almost all steps except “return of medicines”; patient dissatisfaction high (only ~20% fully satisfied).
3 (Indian hospital, general ward) (Annals of Pediatric Surgery) Paediatric surgery general ward; n = 100 sequential discharges Self‐pay group average turn-around time (TAT) ~332 min; credit billing group ~397 min. NABH standard was 180 min (self pay) / 240 min (credit billing) but delays existed. (Annals of Pediatric Surgery) Credit billing (insurance/third-party) patients experienced larger delays. Main delay causes: billing process, insurance processing systems.
4 Pristyn Care survey study (India wide) – “Five out of 10 patients face discharge delays due to medical insurance claims” (ETHealthworld.com) Survey across major Indian cities; size ~4,000 respondents. (Healthcare Radius) ~50% of patients report discharge delay due to claim processing time. ~40% attribute delay to hospital administrative processes; ~25% to lack of coordination between hospitals & insurers. (ETHealthworld.com) Claim/insurance-related delays are major contributor. Even with high cashless coverage, process inefficiencies persist.
5 Delay in discharge and its impact on unnecessary hospital bed occupancy (not India-specific, but relevant for comparison) (BioMed Central) Study of 99 inpatient episodes (elective & acute) in hospital over 4 months Delays contributed 271 unnecessary days (19% of total bed-occupancy days) due to discharge process delays. (BioMed Central) Highlights impact of discharge delay on bed occupancy and hospital efficiency; though not strictly India, gives useful context.

There is no magic bullet, otherwise the problem would have been solved by now. ET article identified a few suggestion to help the process. Among them are:

  • Advance intimation of discharge by hospitals
  • Efficient information exchange to cut down admin delays
  • Investigations by insurers should start earlier

Even others have pointed out other ways but bulk of them focus on the process improvement and better coordination between the hospital, TPA (Third-party Agency) and Insurance provider. There is no doubt that most of the time clearance process is stuck in the interaction chain between those three entities.

But it is also observed that RTLS does enable faster discharge by automating information flow which otherwise are done manually inside the various entities in the hospital.

How RTLS helped Hospitals reduce discharge delay

A hospital in Texas (CHRISTUS Santa Rosa Hospital‑Westover Hills) reported that after deploying RTLS tagged bracelets:

  • The system notified staff of open beds 2 hours 40 minutes sooner than manual entry. Healthcare IT News

  • They calculated this saved 2,339 hours of bed-prep/“pull next patient” time over 12 months for 4,000+ discharges. Healthcare IT News

Here are a few ways a RTLS helps a hospital to reduce the discharge delay:

     Faster bed/room turnover

  • When a patient leaves, an RTLS tag or location sensor can automatically notify housekeeping/cleaning/bed-prep teams, so they start the cleaning/preparation of the bed immediately (rather than waiting for manual notification). This reduces idle time from when a discharge is medically cleared to when the bed is ready for the next patient (or when the current patient exit tasks wrap up).

  • RTLS gives live location/status of patients, staff, equipment; this allows staff to see which patients are ready to go, which beds are vacant, and which pieces of equipment are available. BioMed Central+1
  • Delay in “waiting for transport” or “waiting for discharge summary" or "pharmacy clearance” often caused by lack of visibility. For example, if the system shows that a patient is ready but the porter or transport team hasn’t been dispatched, it causes unnecessary delay. An automated notification enables faster transition between these phases.

    Of course even with all these, bulk of the delay is not going to reduce more than 20% if the friction in documentation sharing, interaction and coordination between the three parties are not addressed. However even if 20% of the discharge time is shortened it creates a big saving for the Hospital in reducing average Length of Stay (LOS) for a patient

    A California Healthcare Foundation study found, "For a 275-bed hospital, reducing the average length of stay by four hours is equivalent to increasing physical capacity by ten beds".
    RTLS is slowly becoming integral to the improved cost and operational efficiency in a hospital. Here we showed how it also improves patient discharge latency.

Responsible AGI and other things


Google apparently in recognition that they lost LLM game (first to OpenAI chatgpt and then to Chinese deepseek) started drum rolling for AGI. Given how people are afraid of AGI, they brought out a paper on responsible AGI (somewhat similar to earlier responsible AI ). Read the Google post here.

Does it mean that LLM ≠ AGI

There were posts earlier where OpenAI said chatgpt is almost AGI. Even some in Google Gemini team said Gemini is almost sentient. But if people are talking about AGI seperately from LLM, perhaps that is acceptance of that fact that LLM may not ever reach human capability of intelligence. In fact there was a recent study that asserted that LLM could not match human ingenuity of ‘zero shot abstract thinking’.
Martha Lewis, a coauthor of the study, tells LiveScience, – while we can abstract from specific patterns to more general rules, LLMs don’t have that capability. “They’re good at identifying and matching patterns, but not at generalizing from those patterns.”  Read the full LiveScience post.

Can the AGI be responsible?

AGI has to evolve from current generative AI framework. Google Deepmind has categorized the challenge into four baskets:

  • Misalignment
  • Misuse
  • Mistake
  • Structural Risk

Misalignment refers to AI model doing something that developers did not intend it to do. Misuse refers to the model being misused by a human controller/user to work as adversary to humanity. Mistake refers to AI model doing something bad without triggering internal checks and balances. Structural Risk ensues from multi-agent dynamics without any fault from individual model. Example for this can be, a new path opens up due to complex interlinking of activities between AI models which developers didn’t intend to.

Of all the four types, Misalignment and Structural Risk are the most difficult to address because they are the most complex and difficult to uncover. We will limit ourselves to the issue of Misalignment for this post. Marcus Arvan explained in a LiveScience post, ‘If any AI became ‘misaligned’ then the system would hide it just long enough to cause harm’. He has given real life examples where LLM shocked the user with the answers.

‘The basic issue is one of scale. Consider a game of chess. Although a chessboard has only 64 squares, there are 1040 possible legal chess moves and between 10111 to 10123 total possible moves — which is more than the total number of atoms in the universe. This is why chess is so difficult: combinatorial complexity is exponential.

LLMs are vastly more complex than chess. ChatGPT appears to consist of around 100 billion simulated neurons with around 1.75 trillion tunable variables called parameters. Those 1.75 trillion parameters are in turn trained on vast amounts of data — roughly, most of the Internet. So how many functions can an LLM learn? Because users could give ChatGPT an uncountably large number of possible prompts — basically, anything that anyone can think up — and because an LLM can be placed into an uncountably large number of possible situations, the number of functions an LLM can learn is, for all intents and purposes, infinite.’ He argues.

Google’s approach to tackle misalignment

Google plans to use second AI model to validate a model’s answer. It’s sort of AI police to police AI model. Can this work? To find answer, we should ask, does policing work for human citizens? You could say mostly. But we should not miss that mostly it’s willingness of human citizens to follow rule and respect police that makes the job of police manageable. We surely cannot assert that for AI citizens. But given that entire AI paradigm works on goal-seeking principle, meticulous control of goal dynamics may provide an understanding of AI motivation.
Demis Hassabis, the Nobel Laureate CEO of DeepMind has a sobering thought about the approach to AGI. In a lecture in Cambridge University, he said, “Lot of Silicon Valley Companies work with the principle of ‘Move Fast, Break Things’ but I think it is not appropriate, in my opinion, for this type of transformative technology. I think instead we should be trying to use scientific method and approach with humility and respect this kind of technology deserves. We don’t know lot of things. Lot of unknowns about how this technology is going to develop. With exceptional sort of care and foresight we can get all the benefits and minimize the downside of it.” He invites people to focus on research and debate now and be mindful that the technology does not really go out of hand.
We are with him on that thought.

Did you know QR code can cut your shipment errors and returns drastically?

QR Code for shipment trackingIf you are a factory owner or goods producers, this may be for you. Small businesses often struggle with shipment errors and returns. Manual processes and human error can lead to picking the wrong items, sending to incorrect addresses, or damaged goods arriving. These issues not only cost money in return shipping and processing fees but also damage customer satisfaction.

QR code can make tracking your shipment accurate and super-easy

A smartphone App is the lightest solution you can imagine. Using the App, you can generate QR code for each shipment and also use the same app to track them from anywhere. Each time someone scans, the location and time is recorded in the App. That way anyone in your office can track the item in almost near real-time.

  • Unique Identification: Each product or package would have a unique QR code attached. This code encodes information like the product ID, order number or any other fields you like to include.
  • Scanning for Accuracy: Warehouse staff can scan the QR code with a smartphone during picking and packing. This ensures the correct item is selected and placed in the appropriate package.
  • Real-Time Tracking: More scan adds more real-time updates. This transparency helps identify potential delays or delivery exceptions.

A 2021 survey by Zebra Technologies revealed that 83% of businesses using QR codes reported a decrease in picking errors.

A 2020 study by Deloitte found that real-time shipment tracking with QR codes reduced shipment returns by 15%.

Incidentally INDTRAC QR App does exactly what is needed. What’s more? We will help you in case you need any assistance in setting up at no cost to you.

Product Launch Announcement

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