Pharmacy Logistics Knockout Reimbursement Schemes
It is pretty amazing how, with only one strike, the pharmacy logistics of an entire country knocked out all the reimbursement schemes and it took two years to understand the problem and try to fix it to some extent. I was so fascinated by this mystery that I decided to investigate and published my conclusions some years back.
I will use the Portuguese example although this one is very similar to other models in the EU.
We have two major payers and several smaller ones. The software is the same in all the Portuguese pharmacies. At the end of the month we close the monthly billing and the computer generates complicated invoices with different copies. We then separate all copies, join the prescriptions with each one, and send them to the National Association of Pharmacy Owners (ANF) and they send it to payers. The major payer and responsible for 60% of the billing volume is the Social Security department. The State co-participates in different ways for all drugs, except OTCs; during the month we sell the prescribed medicines and, five days after sending the monthly billing, the ANF pays us all, and then will retrieve the money from all payers.
Later on, the payers will examine each prescription to see if they comply with the reimbursement conditions; if not, the will send that prescription back to the pharmacy for correction and we issue a debit note, which is annulled when we send back the corrected prescriptions.
Until five years ago Portugal had very few generics and all the medical prescriptions were brand ones, therefore a pharmacy could have six to ten invalid prescriptions, as stated above, and that was considered very normal.
One day, out of the blue, all pharmacies received thousands of invalid prescriptions, issued debit notes worth almost the whole monthly billing, and it went on and on for years and nobody could understand why. That is where, being part of the problem and fascinated by the mystery, I decided to investigate.
The one and only major problem was the drug barcodes. Although we sold drug X as it was perfectly written and confirmed in the prescription, the barcodes were different from those of the doctor’s computer generated prescriptions, and that was why this was happening.
It all starts with data bases. There is the mother data base (INFARMED – National Agency for Medicines) and all the other data bases collect data from this one, but they only collect the data they want, consequently errors are bound to happen. With the injection in the market of hundreds of different generics from all the nine yards of pharma companies, the system burst with so much information.
The other problem is that each pharma company patents all pharmaceutical forms of a given generic (usually 15) and all the dosages (up to 30), but they only have one pharmaceutical form with three different doses available in the market. Furthermore, company T could be on the market for two months and disappear, and that was an unforeseeable event that happened frequently.
When the pharmacy wants to order a new drug, opens the “specialties dictionary” where you will find pills of generic Z, for instance, and several barcodes for each one of them, and selects one, without knowing whether it is in the market, and opens an entry in the system with that data sheet and the computer sends the order to the wholesaler, which, in turn, has a data base of what is really available in the market ; as a result one can say to customers that drug X is not available in the market, because the wholesaler system did not recognize that barcode, but it is, only with a different barcode.
A – National Agency for Medicine’s data base (INFARMED) – mother data base
B – ANF database – collects from A part of the information to put in the “specialties dictionary” that all pharmacies use.
C – Social Security department for pharmacies data base with unknown and random criteria, collected from mother data base.
D – Wholesalers different data bases – for each wholesaler, with the information they want of what is really available in the market, and they sell, and with their own home barcode for “barcode orphan drugs”.
E – Doctor’s different data bases coming from Social Security Department data base with information that no one knows what criteria was used, to collect from C.
F– Each pharmacy own database with the information of what they sell and their own barcodes for the daughters of the wholesaler “barcode orphan drugs”.
As anyone can see, the source is the same for all data bases, but the criteria that the owners of the other data bases follow are all different and information is corrupted so that prescriptions were perfect and it was only a technical error that engineered the thousands of invalid prescriptions.
The “barcode orphan drugs” – because of these errors a wholesaler could have a drug with a barcode that no system would recognize, and so they would give that drug a “home” barcode beginning with number one; if a pharmacy wanted it, they would have to call the wholesaler and ask for the drug’s name, to make another home data sheet entry with a different code also starting with number one, and that is why we then had the daughters of the wholesaler’s “barcode orphan drugs”.
Not to mention the consequences of this problem on the inventory, a data base full of wrong entries, and the headache it was to make the official reports each month, there were/are changes every day with new co participation schemes and new generics prices to add further confusion to the madness we were/are living. Public health was never at stake, each pharmacy has one person continually checking prescriptions to see if there are human errors, and the problem was purely technical.
I started by telling my team to never order a new medicine computer generated but to first call the wholesaler to know what was the right barcode and only when the drug arrived in the pharmacy would we make a new data sheet entry with the right barcode.
Then, I investigated further and saw that there were two system crashes each day that no one notices, but that were/are somewhat responsible for the Merry-Go -Round.
Finally, I performed several barcodes algorithmic analysis.
Example: 88795670 – in this number there are three digits that I know identify the pharma company, the medicine and whether it is available in the market, so, I am the only person that can open an entry from the specialties dictionary without worrying about wrong entries.
I also saw all the other “things” that added further problems with barcodes, such as M&A, new packages, new drug names, etc, etc, I became an expert on pharmaceutical barcodes, but I reduced thousands of “invalid” prescriptions to the average 10.
Notwithstanding the fact that we lived a terrible nightmare for some three years, because it was the billing payment that was at stake, I find it amusing that such a big mess could be triggered by the generics invasion and misleading databases’.
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