12 things you should look out for before buying health insurance
A thread 🧵
You have to be a little wary of policies that come with co-pay. With co-pay, you need to pay a pre-decided amount from your own pocket whenever you make a claim.
The larger your expenses, the larger you’ll end up spending your own money, even with insurance.
This is something you need to be conscious of when making choices during hospitalization. Let’s deep dive into what will happen if your policy doesn’t have a room rent limit and when it does.
You have a health insurance cover of ₹15 lakhs, with *no room-rent limit*. You get treated for a condition that costs you ₹2 lakhs, and you paid ₹15,000 in room rent per day.

You have a health insurance cover of ₹15 lakhs, with a room rent limit of ₹12,000 per day. You get treated for a condition that costs you ₹2 lakhs, but you stayed in a room that costs you ₹15,000/day.
In this case, each component in your cost is proportionately deducted (12/15*cost), and therefore, you can only claim ₹1,60,000 of your total expenditure.
Check the attached image to understand how your claim amount gets reduced.

You might have thought the room rent doesn't matter as much - but it does.
During this period after you purchase a policy, you cannot make any claim. It’s typically between 30 to 90 days. This waiting period is waived in the event of an accident that leads to immediate hospitalization.
This applies to pre-existing diseases (like diabetes) present when a health insurance policy is bought. The insurer will accept your claims for treatment related to these diseases only if the waiting period you agreed upon has passed.
This means that the insurer applies a limit on the amount you can claim for treatments for specified conditions and diseases.
Save yourself that shock by going through the rest of this thread and also bookmarking it :)
After you buy a policy, you can cancel it within a certain free-look period, stating your reasons.
However, the insurer will not refund any medical tests, stamp duty charges done during this period.
The free-look period is typically 15 to 30 days.
This is the facility given by the insurer to you, for covering your pre and post-hospitalization expenses.
This will go a long way in saving you a lot of money (and stress!)
This means single-day treatments like eye surgery, etc. are covered by your health insurance policy.
Several common treatments require single day hospitalization, so you need to totally make sure that these are covered.
If you don't make any claim in a year, the insurer will reward you by increasing your coverage amount (with a max limit).
No claim bonuses are good-to-have on your policy.
If your policy has a domiciliary cover (quite some insurance-speak!), it means that you can claim money when you are treated at home.
This is great during these times because it’s really difficult to find hospital beds during the pandemic.
If you’ve completed 8 years of coverage, the insurer cannot reject an eligible claim, for any reason except for proven frauds and exclusions made in the policy documents.
If you spot these words, it means the insurance company provides you with a free health check-up.
More from Health
You gotta think about this one carefully!
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇

Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
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Ivor Cummins has been wrong (or lying) almost entirely throughout this pandemic and got paid handsomly for it.
He has been wrong (or lying) so often that it will be nearly impossible for me to track every grift, lie, deceit, manipulation he has pulled. I will use...
... other sources who have been trying to shine on light on this grifter (as I have tried to do, time and again:
Example #1: "Still not seeing Sweden signal versus Denmark really"... There it was (Images attached).
19 to 80 is an over 300% difference.
Tweet: https://t.co/36FnYnsRT9
Example #2 - "Yes, I'm comparing the Noridcs / No, you cannot compare the Nordics."
I wonder why...
Tweets: https://t.co/XLfoX4rpck / https://t.co/vjE1ctLU5x
Example #3 - "I'm only looking at what makes the data fit in my favour" a.k.a moving the goalposts.
Tweets: https://t.co/vcDpTu3qyj / https://t.co/CA3N6hC2Lq
He has been wrong (or lying) so often that it will be nearly impossible for me to track every grift, lie, deceit, manipulation he has pulled. I will use...

... other sources who have been trying to shine on light on this grifter (as I have tried to do, time and again:
Ivor Cummins BE (Chem) is a former R&D Manager at HP (sourcre: https://t.co/Wbf5scf7gn), turned Content Creator/Podcast Host/YouTube personality. (Call it what you will.)
— Steve (@braidedmanga) November 17, 2020
Example #1: "Still not seeing Sweden signal versus Denmark really"... There it was (Images attached).
19 to 80 is an over 300% difference.
Tweet: https://t.co/36FnYnsRT9

Example #2 - "Yes, I'm comparing the Noridcs / No, you cannot compare the Nordics."
I wonder why...
Tweets: https://t.co/XLfoX4rpck / https://t.co/vjE1ctLU5x

Example #3 - "I'm only looking at what makes the data fit in my favour" a.k.a moving the goalposts.
Tweets: https://t.co/vcDpTu3qyj / https://t.co/CA3N6hC2Lq

1/OK, data mystery time.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.

4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.