EFNN, The Tiger Financial News Network, TFNN, Headline News Update. Good morning everyone, this is the Thursday 10 o'clock Tiger Financial News Network Market Update on this 28th day, last trading day of March and we're looking at the Dow up 17 at 39,076, it went all the way to 39,848 early this morning, 39,889 is the all-time high, we'll still the days young, we'll still try to tackle that, we're going to be watching it closely, the weekly chart is still looking very good, if it goes to a new high today that will make it a leg C, extension of the weekly chart, amazing how these things have worked and that nine period moving average over the 14 is still very strong looking at the S&P, S&P is just a little bit stronger than the Dow, it's up 5.21 at 5263, it is 5261.10 was the all-time high so it's just a you know a couple of points away from that all-time high looking at the QQQ, trading up 60 T cents at 445.50, not bad action, it's holding the higher range and that weekly chart will make a peak death, there's no new all-time high today but in the meantime it's been very strong, IWM the Russell 2000 is up $1.09, add a new recovery high, this is a very good action at 210.77, gold is acting extremely well, it's up 15, looks like it wants to maybe next week it goes to this leg D above the continuous contract high of 22, if I can just get the mouse there we go, if I can get that rare as 2247.10, it goes above that starts the leg D and that'll extend the leg D in the weekly chart, holding very nicely 9 over the 14 strong, let me go to Crudall, Crudall is trading up about $1.06 at 82.41, this is really good action, it's really helping some of the service oil, service stocks etc in this in the oil sector, let's see the bonds, bonds right now are trading up $3.30 at 120 and $9.30, it's had a bit of a balance and that means the yields are coming down a little bit but not very much, I'll be back for the Tiger conditions hour and I want to wish everyone a good Friday, I'll be back in a few minutes for the Tiger conditions hour, check out our opening call daily newsletter, see you in a few months.


lecture 41 okay and we were looking at the last thing we were doing was looking at OFDM in the presence of ISI and we were looking at a discrete time model and this is how the model looked okay so my the overall system is going to have symbols coming in okay so I will call it SK then your first thing you do is a serial to parallel conversion okay so you have S of 0 all the way to SN minus 1 then you do what then you do one what's the next step IFFT right so it's you do an IFFT N and point IFFT and this gives you a set of intermediate symbols okay I think I called it X tilde X tilde 0 to tilde N minus 1 okay and okay so in order to deal with ISI we came up with this suboptimal idea of converting doing what converting linear convolution to circular convolution on a block like I said it's a suboptimal idea but it works okay so you repeat from N minus L plus 1 all the way to X tilde N minus 1 after that you do the same transmission as before okay so once you do that you all your all you do next is a parallel to serial conversion and then you transfer okay so this goes out and the total number of complex numbers that you will be sending out will be N plus what L minus 1 okay so that's what you'll be sending out okay I don't want to call them symbols I'll say complex numbers okay so so so this will go through D2A converter right with the sync interpolation at at the rate of T by N okay and then you send it out up convert and then send it and then you don't convert and then you do anti-aliasing filter followed by a sampler then you'll get a sequence of symbols corresponding to each transmitted symbol okay so all that we are modeling as a filter HK okay so I might have done everything with N did I do everything with N okay so let me do okay so you have HN and then noise getting added okay so the point of adding the cyclic prefix was to make sure that this linear convolution with HN becomes a circular convolution on a subset of the numbers that you are sending out okay so you do a on the received values here you do a serial to parallel conversion okay you will get received values corresponding to the cyclic prefix which you ignore and then the received values corresponding to the actual transmitted numbers that I am going to call y tilde 0 so on down to y tilde N minus 1 okay and and I said I am going to write the vector of y tilde 0 to y tilde N minus 1 as a matrix times the vector x tilde 0 to x tilde N minus 1 okay and I said this this channel is this L tabs H of 0 to H of L minus 1 okay so if you start writing out that you can do that very carefully see y tilde 0 maybe I'll show y tilde 1 N minus 1 it's going to be equal to what will be the size of this matrix N by N right so it's got still does 0 x tilde 1 so the first row is going to be what okay so first row is going to be a convolution output at this point right so this is where you have to see okay so the convolution output at that point is the first row so H of 0 multiplies x of 0 then H of 1 multiplies what x tilde N minus 1 then H of 2 multiplies so on till H of L minus 1 which multiplies x tilde of N minus L plus 1 okay so that's the remaining entries will be 0 okay so what about the next row it'll be a cyclically shifted version okay so you can show that it will be this think about it and carefully make sure that you can understand that 0 H of L minus 1 this thing will be H of 2 okay it just comes from the simple linear convolution formula but since we repeated the values once again even the first thing becomes a circular convolution if we did not repeat it what will be the first row will simply be H of 0 followed by all zeros since I repeated it the other entries also enter the picture okay so to complete this all you have to do is just repeatedly do this n times if you do it n times this row is going to end at L minus 1 right afterwards you'll have 0 okay so the last row to figure out the last row you can take the first row and do what rotate shift left okay so that's an easy way of figuring out what the last row has to be so that will work out as H of L minus 1 all the way down to H of 0 okay so the last this guy will be some H of L minus 1 here followed by 0 okay so there will be a H of 0 line coming through also okay so you can check all okay so so this is the this is the matrix representation of this and this becomes a circular matrix circle and matrix with the first column being the channel tabs okay so what's the next column next column is a cyclic downshift of the first column likewise that's another way of viewing this matrix these are all also a version of topletes matrices this these things have these matrices have very nice properties okay which you use quite often okay so now there are several ways of describing FFT the FFT matrix and a circular matrix are very closely related okay so what's the relationship yeah so if you the every column of the FFT matrix is an eigenvector of this circular matrix what's the eigenvalue it's the value of the FFT at that point okay so that's the relationship but let me show you how that works out so I'll not I'm not going to prove that relationship here I'm assuming you know that I'm going to simply write down what the answer is okay so it turns out the circular matrix can be written as let me be careful here so I need the right kind of expression FFT FFT and inverse times a diagonal matrix times FFTN of yeah this is the way in which you can write the circular matrix okay so what goes on the diagonal is capital H of 0 capital H of 1 all the way down to capital H of n minus 1 and what's capital H of k is I think summation they'll be they might be 1 by root n okay n equals 0 to n minus 1 H of n e power minus j 2 pi n k by okay so this is a very standard result which you've used all the time in FFT right circular convolution of two sequences becomes the product of the point-wise product of the FFTs okay so it's the same result read in another another way okay so now going back to our so this this is all 0 going back to the previous equation the vector y tilde 0 to y tilde n minus 1 becomes what okay becomes FFT inverse times this matrix times FFTN x okay so I'm going to pull the FFT inverse to this side okay so you get FFTN times this equals a diagonal matrix H of 0 H of 1 H of n minus 1 0 here 0 here times what FFT times x tilde what is FFT times x tilde the vector of s okay remember how did we get x x was the if FFT inverse of s so if you multiply that out you'll simply get the vector of s s 0 2 s n minus 1 okay so so so this equation suggests an obvious way of processing the the vector y okay the y vector should be done what should you do do it in this picture what should I do to this y it should take FFT okay if I take FFT at the output I can expect each entry to be a scaled version of the symbol scaled by some complex number okay so it's like a one tap channel for each symbol each symbol went through a one tap channel and you do a simple one tap equalizer for each subcarrier separately followed by a slicer independently you can decode the symbols okay so that's the that's the story of the receiver so at the receiver okay you have y tilde 0 y tilde n minus 1 which goes through a FFT n and then you do one tap equalizer to get your uh then you slice your s hat of 0 to s hat of n minus 1 that's console complex no yeah that is the one tap equalizer that's the definition of a one tap equalizer okay one tap equalizer will basically find the scaling if you train it so even the agc for instance right the automatic gain control that you might want to do can be pulled into the one tap equalizer it's enough if you do that as long as you have a training phase you can determine the scaling it's really easy to do that okay so this is a very simple equalizer okay so I wrote it down as if this is an optimal way of processing and dealing with isa clearly it's not optimal okay so it's far away from being optimal and this is a major overkill but look at the huge advantage for the receiver okay receiver doesn't change at all okay and only thing you have to do is an FFT which is which has been researched so deeply that people have very very efficient implementations of FFT and uh in digital okay so it's very easy to get nice uh what are called IP course for doing these FFTs it's very easy to get that and that very low power implementations are available so once you do that beyond that it's a simple one tap equalizes nothing in fact even the tap usually you don't train there's some pilot some pilot assisted thing available for which the tap is pretty much given to you okay so you know what that is and all you have to do is slice on each uh subcarrier and on top of that it gives you so much flexibility at the transmitter you can put different type of alphabets on different subcarriers depending on the strength of the tap okay if you see that the SNR on on one subcarrier is higher then you maybe do a larger constellation so you can you get all that flexibility at the transmitter the flexibility of working with a set of parallel channels okay so that that's a huge advantage and because of that OFDM is really really popular today both in wireless applications and even in wireline applications like DSL OFDM is very very popular it gives a lot of good performance and people have studied capacity closely and you can even show that it approaches capacity also when if n is very large the penalty you pay in terms of the cyclic prefix will become negligible okay so if n is large then it's okay if n is small then you're paying two penalties okay one is the decrease decrease in the data rate okay the data rate overall decreases by a fraction n by n plus l approximately okay right you not every symbol you're transmitting carries data in fact the cyclic prefix is not used at all at the receiver you just throw it out okay so only a smaller fraction is there so that's one penalty and another penalty you pay is you have to actually transmit those symbols okay so that again consumes power at the transmitter okay so you pay double penalty but in spite of that this is very popular because of the simplicity of the receiver okay so this kind of equalization is also called frequency domain equalization so instead of dealing with ISI in the time domain by building filters maybe to minimize mean square error etc in the time domain completely you kind of move comfortably to a frequency domain where to the frequency domain where the equalizer becomes one tap for instance if you choose the numbers carefully okay but one more thing I have to point out is the cyclic prefix while use well while it's totally lost from a data recovery point of view can be used for symbol timing recovery and all that because it's the same repetitive pattern which shows up at the interval of at a certain interval okay so you know the same pattern will appear again so you can look for that and then figure out some timing information from that so that's done sometimes okay so that's useful so the cyclic prefix has some added benefit in a implementation sense okay however there's one practical problem because of the OFDM symbols okay so your so that a final transmitted signal after you do the D2A and in complex baseband right when and then so the analog signal is going to actually be a sum of several sinusoids okay so when you take some of several sinusoids what ends up happening is what's called the peak to average ratio becomes very large okay so one way of thinking about it is the sum of several sinusoids will typically tend to a kind of a Gaussian distribution for values okay so you can show these things it's possible to show the different amplitudes and faces if you add a whole bunch of sinusoids it will tend towards a Gaussian distribution when you take samples okay so on these Gaussian distributions if you take a whole bunch of values and look at the maximum value as opposed to the average the maximum value can be fairly large okay so it's possible to find all these things okay so the peak to average ratio is going to be large for this this X of t so what so what's the problem I'm sorry yeah so the problem will come at the power amplifier level the point is whenever you design whenever you computed SNR do you speak power or average power use average power right so that's how we computed it was always expected value of x squared so it's always the expected value it's the average power and all the theory you do is for the average power okay but the power amplifier has to deal with the signal which goes all the way to the peak amplitude and hopefully doesn't distort okay so you want a linear response for the peak amplitude and if your average to power peak ratio the peak to average ratio is very high then it puts extra strain on the power amplifier okay so these things become more expensive so there are all kinds of techniques you'll see a lot of research trying to reduce what's called the PAPR for OFDM signals peak to average power ratio okay so that's that's one penalty you pay from a very practical implementation level but obviously it can be overcome and it can be done it can be overcome in several ways in fact sometimes people tolerate non-linear distortions in the power amplifier and compensate for it with a similar non-linear processing at the receiver so that's also possible so you can do some some such thing okay so so that's OFDM so in short OFDM is a block modulation scheme okay so instead of doing serial PAM you do block modulation scheme and it gives you it's it's easiest to understand it in the frequency domain okay so even though it's not FDM even though the subcarriers and the signals used per subcarrier overlap in frequency okay it's easiest to understand it in the frequency domain it gives you rough separation in the frequency in the frequency domain okay it still overlaps but by clever baseband processing you can completely separate the signals even in the presence of ISI by adding a suitable cyclic prefix so because of all these things OFDM is very very popular okay any questions questions or comments about what this is okay so so if you take any of today's wireless standards okay or the future wireless standards they will all for sure use OFDM so it's no doubt about it OFDM both in the uplink and a version of it the downlink and the version of it in the uplink also so we use OFDM everywhere okay it gives a lot of flexibility okay so i think yeah so what happens is when you do when you're a lot so so so i've been talking about this flexibility of putting different kind of alphabets on different subcarriers okay so usually in systems when you actually design it you expect some feedback from the receiver to tell you these are my good subcarriers these are my bad subcarriers so the bad subcarriers you can show even from an information theoretic capacity maximization point of view that in the bad subcarriers you should not put anything so you don't put anything in the bad subcarriers so there'll be no data coming there so you use only the power it's best used that you have is best used in the subcarriers that are good okay of course there are other constraints to look at it look at but usually that's how the power is allotted okay so in fact if you have a notch you'll allot zero power to that subcarrier so that kind of flexibility you get with OFDM if you're doing completely in time domain adjusting your signal spectrum to look like that is not so easy there are clever ways of doing it but it requires too much of processing at the receiver here this receiver doesn't do anything only does FFT so that's the main advantage any other question okay so I think that brings us to the end of OFDM and I'm going to so for the I think the only thing that's left is to talk briefly about coding okay so what we'll see in the next few lectures is a brief introduction to error control coding as it's used in digital communication systems and we'll see some very simple examples and then we'll see convolutional codes as the main coding technique okay so I won't introduce anything else only convolutional codes so so I think we have roughly four or five lectures left maybe maybe I'm over counting it a little bit and well actually there's more and after that after four or five lectures I'm going to do tutorial sessions okay and yesterday there was an exam a few people wrote it I think so maybe I should put out solutions for problem 7 so in the problem set 7 there are two things for which you have to think a little bit what are the two things so I've asked you to formulate the equations for the zero forcing linear equalizer and the MMSE DFE okay so I think most people who wrote the exam yesterday didn't formulate the equations for zero forcing linear equalizer did you or didn't think some people might have done it but some people did do it so it's very easy zero forcing linear is very very easy right so it's enough to make sure that some things go to zero so maybe maybe I'll put out a solutions solutions for that sometime tomorrow or day after okay so next week also we'll have an exam on the on problem set 8 but I think 8 has very few problems so maybe I'll add few more problems to it and then there'll be then maybe there'll be one problem set on the OFDM OFDM things and then one more encoding and with that it'll be over okay so we'll have a total of 10 problem sets to deal with okay so let's move towards coding okay so but before that so okay so the first thing is about the terminology okay so you always talk about error control codes and the thinking is you design a digital communication system and then if there are errors the errors can be corrected by error control codes yeah of course that's a very good way of viewing error control codes not wrong that is also possible but today people have not just today even traditionally the best way of understanding the role of error control codes in digital communication system is through what's called the notion of coding gain okay okay so so far we have never talked about any signal processing technique which provides something called coding gain okay so what is coding gain what is gain okay so so far we always did some processing okay whatever processing we did and then we saw the probability of error as a function of SNR okay for that processing okay and then we took whatever it gave right so maybe there was an optimal way of processing it but ultimately we just took probability of error as a function of SNR as based on the analysis whatever it gave you okay so none of the techniques really give you a method to improve on that trade-off okay how can I get the same a better probability of error at the same SNR how can I get what's the other other thing okay how can I get the same probability of error at a much lower SNR okay so those are two things which are important trade-offs if you remember the very first class we talked about these trade-offs in many in any communication system ultimately there will be one plot what will be that plot there will be some error rate here okay so maybe just to be specific I'll say some bit error rate versus what's called eb over and not okay so it's what I've been talking about energy per information bit divided by noise okay so the energy you're spending per information bit or noise I mean power per information bit divided by noise okay so and and then you make a plot okay so if you take for instance QAM or QPSK or BPSK okay it's a very good example then you make a plot here this point will be around what say 10 dB when this is 10 power minus 6 okay so these are plots okay and if you have ISI then this plot might shift to the right or left or it might worsen usually it will shift only to the right or it will stay where it is okay so that's what will happen okay and okay so the so I somehow went into ISI maybe I'll come back and address the issue of ISI later but for now if you imagine there's no ISI or anything the most interesting thing to look at in this curve is what the question most important and interesting question to ask with respect to this curve is how do I move this to the left okay so far we have never seen a method to do that an error control codes pretty much are the only method available today to move this curve to the left okay so in that way coding should not just be thought of as correcting errors it's the fundamental tradeoff between eb over and not n bit error okay so in fact in Shannon's first paper on theory of communication he had codes and he showed what's the best tradeoff is okay so that's what the whole thing about capacity is okay so codes form part and parcel of communication systems and but the side effect of that is the problem with that is it requires more complicated receiver processing of course right without getting any gain itself we saw equalization was fairly tough okay so receiver processing is required so if you're getting coding gain then obviously processing will become difficult so that's why it was not so popular for quite some time okay once with advanced implementation possibilities today people of course obviously implement very complicated error control codes in today's communication systems okay so and so study of digital communication is not really complete without looking at error control codes for that purpose we'll quickly see it okay so if somebody asks you what the main purpose of error control codes is is to move this curve to the left provides you a method for moving this curve to the left okay so it turns out for instance for if you look at bpsk or something this curve can move all the way to zero very close to zero okay right so if you can come very close to zero so gains of eight db and nine db are theoretically possible with error control codes okay so eight and nine db are huge numbers okay billions of dollars okay so maybe billions of dollars doesn't mean anything now but but anyway so that's a number to keep in mind okay so so so we'll we'll study this and for very simple situations okay so we won't study it in the most general way the model that I will take for for looking at error control codes is the dpsk over awgn model okay I might briefly hint at other constellations okay but mostly I'll be talking only about bpsk over awgn so how does this model look so you make bits no isi okay what do you do if there is isi yeah equalize and once you equalize if you look at the input to the slicer from your symbols to the input to the slicer it's going to be roughly bpsk over awgn if you're doing bpsk with isi also right so that's the logic behind just sticking to bpsk over awgn so all these other ugly signal processing techniques you use to get to the slicer we will not look at right now we've already seen it you know how to do that okay so since we've already seen it we'll only look at this part for the coding game okay so bits go through a mapper zero goes to plus one one goes to minus one okay and then you produce a symbol sequence okay so the symbols are plus minus one and then what what what happens to the symbol sequence noise gets added to it remember it's all discrete time model okay so this noise will be normal distributed with zero mean and some variance sigma squared okay and then you get a received value which you have to process okay so so far we've been just talking about slicing the received value okay so maybe soft slicing okay so soft slicing or hard slicing hard slicing will mean or the output of the slicer you decide whether it is zero or one soft slicing will be you provide the LLR okay so LLR in this case is very simple right so what's the LLR if you have a received value R the LLR will work out to two times R by sigma square okay so it's as good as R okay so so if your receiver works with R itself it's said to do soft processing such receivers are called soft receivers okay so if it's working with the quantized version as in zero one bit quantized version we make a slicing it's called a hard receive okay so we look at both situations we in when we do coding we might want to sometime process the soft values directly either the LLR or the receive value R itself or we might want to process the hard decision values okay so we'll do both okay so this is this is the model we'll be dealing okay so like I said if you have other complicated parts you deal with them in whatever way you want to get to this model then we'll do coding with this model okay so that's the situation okay so when you don't do any coding each symbol is sent independently one at a time and you can decode each received value there's nothing better you can do right if one symbol is independent from the other symbol there's nothing you can do so what you do in coding is you introduce some dependence between symbols that you are transmitting okay so that's the basic idea behind coding when you do coding you add controlled redundancy okay so I'm saying redundancy because it's redundant you're repeating something okay so well you'll repeat it very smartly so it's controlled in a certain way okay so you don't necessarily do a repeating okay so if you do coding okay if you do coding okay so first thing I want to show is an uncoded system so once again from the same picture as before okay so you have a bit b of k going to a symbol s of k noise gets added to it n of k you get rk so at rk if I want to define snr and eb over n0 we know how to do that okay so snr is going to be what okay symbol energy is 1 right in bpsk noise energy is sigma square okay so if you want to do eb over n0 what will happen there'll be a factor of 2 that enters from somewhere right because n0 is 2 sigma square so you will get what you should get 1 by 2 sigma square am I right okay so these are the definitions so in this model you know exactly how to compute snr and eb over n0 okay and remember this snr and eb over n0 has a practical relevance in the in a real system also okay maybe not in absolute terms but in relative terms it definitely has a real meaning right so when we convert from symbols to wave forms we keep track of that we make sure the energy is the same right and the sigma square can be also obtained from the noise spectrum at the receiver so all these things are very realistic numbers okay but from a model point of view I'm pointing out what this is okay so usually this is converted to db when you convert to db what do you do 10 times log 10 okay so that's a very standard okay so so if you notice this thing which is actually es energy in the symbol was equal to eb which is energy per information bit okay because every symbol carries one information so this is the no coding picture okay so this is no coding and when you do this you know exactly what the tradeoff between probability of error and eb over n0 is what is the tradeoff probability of error is going to be q of 1 by sigma which is square root of 2 eb over n0 okay and you can plot this and you'll get the previous plot I said I showed you okay there's nothing better you can do okay so the idea of coding is to not send one bit at a time independently but to collect a bunch of bits add some control redundancy and then send all the symbols together okay and then look at all the received values together and try to do collective decoding now you can't just slice independently you have to do collective decoding okay so that's the idea and this is how the picture looks okay so I'm going to change a few things here so I take a vector b say which is k bits and then first do something called n coding to get a vector c which is say n bits okay so usually you pick a rate k by n which will be less than 1 okay so because you are repeating it okay so the k bits get converted into n bits okay so one way to think of it is c will be composed of two parts b followed by a vector p b will be the same k bits and then p will be additional n minus k parity bits that you added okay so this is how coding is done in most cases in practice okay so there are very few cases where you don't do this in fact actually there are cases where you don't do this also okay so so in fact there's no need for you to repeat b it can be any set of n bits okay so this c is said to be a codeword okay so this this is called the message this is a codeword okay so the message b belongs to okay so I've not used this notation before what is this notation 0 1 raise power k what is that okay set of all k tuples binary k tuples so any k bit vector okay c actually belongs to 0 1 power n see notice the message can be any 1 of 2 power k possibilities but the codeword since you are doing a definite mapping will be only what the list of all codewords will be a subset of 0 1 n okay so this is the codeword this is there and the code is the set of all codewords it will be a subset of 0 1 okay so what does the encoder do now encoder does a 1 to 1 mapping between yeah the message from the message space to the code space the code okay so that's so that's what the encoder does and typically the mapping is done this way all right so k is less than less than or equal to 1 less than or equal to n and that's a reasonable choice in most cases okay so this is encoding once you do encoding the rest of the model does not change okay so next you do mapping 0 to plus 1 1 to minus 1 okay so if your codeword bit is 0 you send plus 1 the same bpsk mapping as before if the codeword bit is 1 you send minus 1 there's no change there and noise gets added to it you can't control this okay so there's too many ks here so we'll simply write noise okay noise gets added to it normal mean 0 variance sigma square and then you get a received vector r okay so this r is going to be r 1 r 2 so on till r n right r will be what some some real real vector okay n n tuple real numbers okay so the first question I want to ask before we look at how to do any decoding or anything like that is to define a suitable eb over n0 for the coded situation okay we have to have to carefully do it why exactly the information is one bit of information is not carried by each symbol less than one bit of information is carried in each symbol how many bits of information is carried in each symbol k by n so it's like everybody knows the answer okay so eb will become what now n by k right so eb will become n by k okay so you notice in the previous case eb was equal to 1 here it's become n by k it has artificially gone up because I'm sending lesser information in one thing okay so eb over n0 will be what n by k 2 sigma squared okay another way of doing that this rate is typically denoted r so 1 by 2 r sigma square is a typical computation for eb over n0 okay so if you are doing coding at a certain rate r say half or one-third or two-thirds or 0.9 or whatever number that is then your eb over n0 definition changes compared to the uncoded situation so if I did a computation for probability of error with a certain eb over n0 here at the same eb over n0 I will have a different value of what sigma in my simulation model okay so if this is a kind of a loose simulation model or the way you're thinking about it at the same eb over n0 the sigma for the coded situation will in general be will be larger right so that's the right way of comparing the coded and the uncoded systems so this is a very fair comparison between coded and coded systems okay if you have a certain eb over n0 in the uncoded case each symbol is carrying more information so I use a certain sigma if I now do rate half coding at the same eb over n0 I have to use a larger sigma to break a fair comparison okay so when coding gives you coding gain it has to work against a larger noise larger noise that's being added so in spite of that coding gives you a huge gain okay so that's the way to think about it okay so in practice there's another way of thinking about how this noise enhancement comes about okay suppose you have you have to send k bits and you're using a certain clock okay certain clock rate and you have to send it within one second so use a certain clock clock rate now within that one second you have to send n bits which means your clock rate would have increased by n by k so why was the noise increase you're using a larger bandwidth right obviously if your clock rate is increasing by n by k you're using a larger bandwidth so in a receiver you'll have to filter a larger bandwidth which means what you're letting in more noise and the noise variance is increased okay so that's one way of thinking about it any other way you deal with to make a fair comparison between coding and encoding you have to pay this penalty in eb or n you cannot do it without makes total sense okay so that's the thing I described of increasing the clock rate is one very practical way of doing it and most systems that's how they do it they would keep a certain uncoated rate and then they would introduce a coding block and increase their clock rate use more frequency let in more noise and deal with that in spite of that coding gives you gain in eb over n naught with this type of eb over n naught calculation coding can give you enormous gain okay so that's the way to understand it in the digital communication system okay so beginning with next week we'll start with some very simple coding schemes which don't really give you coding in okay but just to understand the decoding better okay we'll do that first and then we'll slowly move on to convolutional codes which give you a lot of coding


An explosion has occurred on a Russian army ship transporting caliber cruise missiles in the Crimean city of Sevastopol, Ukrainian telegram channels reported. According to the report, the incident took place on board of the 21,631-by-an-m small missile carrier. The sound of the explosion was heard in different parts of the city and alarm systems have been activated in cars. Governor of Sevastopol Mikhail Razvozeev said the smoke had been caused by military exercises held on the ship. According to some sources, there are casualties as a result of the incident. The victims have been removed from the area by helicopters. The footage spread on telegram channels shows ambulances moving in the area after helicopters.


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What's good you guys this your boy Chihuahua back at y'all again with another video and today I'm finna do a custom piece of Drake so without further ado let's jump right into this video and there you have it you guys the custom Drake piece is completed y'all let me know in the comments who you would like to see me draw next you know I'm saying it create a dope you know look collage with them um thank you guys for watching make sure you stay tuned because Moheed is coming soon and I'm out this thing


While he's doing that, I'm going to call this meeting to order. Welcome to the Green Mountain Care Board. My name is Kevin Mullen, Chair of the Board until tomorrow. This will be my final meeting. I have some very exciting news for the public that the governor has named Dr. Jessica Holmes as Interim Chair, and she'll be serving as Chair of the Board until a permanent chair is appointed. Congratulations, Jess. I know that the Board and the public are in very capable hands. With that, I am going to turn it over to Susan Barrett for the Executive Director's Report. Thank you, Chair Mullen. I have some updates on public comments and then updates on our schedules. We are currently accepting public comments on the FY23 hospital budgets until August 30th. We'll be accepting those comments. We ask that you please put those comments to us, either through the portal or give us a call or you can email them to Cara by August 31st so that the Board can consider the comments and their decisions on the FY23 budgets. We also have an ongoing public comment period regarding a next potential model with the federal government, an all-payer model with CMMI, and please submit any comments regarding that next potential model to us. We are sharing those comments with HS and with the governor's office as they are leading those negotiations. Then scheduling, I want to let folks know that we will not have a regular board meeting next week. That's August 10th. However, we start in earnest the next week with hearings for the FY23 hospital budget. Please check our website for the hearing dates and the different hospitals coming before the board. Cara just put that schedule under What's New, which is on the landing page of the website. It's really handy for everyone to see the schedule for the hospitals and those start the week of August 15th. With that for the last time, I will turn it back to you, Mr. Chair, and just say thank you very much for all that you've done for the board for leading our staff over these last five years. You've done amazing things at the Green Mountain Care Board, and I just want to thank you so much and we'll miss you. Thank you, Susan. I'm going to miss everyone as well. The next item on the agenda are the minutes of July 27th. Is there a motion? So moved. Seconded. It's been moved by Member Pelham and seconded by Member Holmes to approve the minutes of Wednesday, July 27th, without any corrections, deletions. Is there any discussion? Hearing none, all those in favor of the motion, please signify by saying aye. Aye. Any opposed, signify by saying nay. Let the record show the minutes passionately. So with that, the main purpose of this afternoon's meeting is to look at different tools that are available to the public and to the board for looking at pricing, and I'm going to turn the meeting over to the leader of our health system finance team, Sarah Lindberg. Sarah, at least I thought I was going to turn it over to Sarah. Susan, do you know where Sarah is? Well, let me go around the corner because I believe she's in our office and I'll see what's going on. Hold one moment, please. Yeah. Apologies for having some technical difficulties here. So my computer is frozen. There you go. I thought you just skipped out on us, Sarah. Yeah. I'm going to have to reboot my computer though. It just froze up. So I'm on my phone. So just one moment, please. I'll be right back with you. Thanks. Okay. She was just so sad, Kevin, to see you going that she just couldn't face the audience for a while. Kevin, we are going to miss you, and we just so appreciate all your service over all of these years, not only in the legislature, but in the Green Mountain Care Board, and you're truly a dedicated public servant, and it's been a pleasure to serve with you. I really do appreciate your leadership and everything you've done for Ramoners. Thank you, Jess. Mike Taltrecco, I see you have your hand up. Maybe you could kill some time while we're waiting for Sarah. It's an important time to kill. Do you have a good joke, Mike? I don't have a camera today. I apologize for that. But I may not be able to stay to the end of this meeting, but I too wanted to express my gratitude for all the hard work and your contribution really to the board, but more so in your leadership at a long time Senate position. So Kevin, congratulations, and I hope you enjoy your retirement in a different way than some of the other people that I know that have recently retired. So enjoy your families important and go embrace that time that you have with them. Thank you. I would say, Mike, that those people failed at retirement, and that's my biggest fear.  So with that, I see that Sarah is back and Sarah. Yes, that probably feels staged, but I know how much you value punctuality, Tia Mullen, so I didn't plan that. So are you able to see my slide here? We are. Okay, great. This is more my old data hat than my current finance team hat and just a transitionary presentation, but as the budgets come before us, I thought it'd be a good time to look at some of these different tools that are available. The reason I put price transparency in quotes is because it's a term that a lot of people use, but can mean a lot of different things, and so we can explore that here today. So the agenda will review some terms. Language is really important whenever you're talking about any of these issues. Just review some of the established goals that people consider when they are interested in comparing variation and cost repayment. Then the tools we're going to review today are both the hospital and the plan price transparency requirements from CMS. The NASHP hospital cost tool, the RAND Corporation pricing studies that have been released in several iterations, and a couple of our own tools, one being the payment and cost variation work that Burns & Associates did for us as well as the reimbursement variation report. So probably the most triggering word in this area is cost, because it means totally different things to totally different people, and truth is very relative. So if you're a patient, likely you're thinking about cost as what's coming out of my pocket to take care of my medical care, and that's going to be things like co-insurance, co-pay deductible premium, but it also might include less tangible things like the time you have to take off work to get that care or the transportation costs to get there. So when you think about medical costs, it can be quite a elastic bucket of services and expenses for a consumer. When we think about providers, usually we're talking about hospitals, but not exclusive to hospitals at any means, but that's the expenses they're incurring to provide this medical care. So equipment, the staff, compensation, infrastructure like having to plow the driveway or the time it takes to shovel your front walk, and all the supplies that come with providing the care, licensure, medical malpractice insurance, these are all expenses that providers are incurring in order to provide that care. So they're probably thinking costs in a more kind of time in materials kind of way than a consumer might be. And for insurers or the health plans, they are thinking about the cost for that service as that allowed amount, which is the negotiated rate between them and the provider. And they also have administrative expenses to maintain those services, adjudicate benefits, monitor for fraud and abuse, things like that. But these are all different costs for kind of the same interaction in the system. So the truth is that it's very hard to determine the cost of any one interaction in the healthcare system. And so we'll try and focus on things that are easier to define and measure than things like costs. So I'm gonna use the word charges and I'm as guilty of anyone of slipping on language. I'm gonna do my best to stay disciplined, but the charges would be the full amount that's billed by the provider. A reimbursement would be the actual payment made to a provider by a patient or a health plan. And then there's the expected member share. So that's the cost sharing that happens usually for a medical encounter. And that's what the hospital is expecting to get. They may or may not are the provider I should say, but that is factors into that equation and some expenses providers take on is trying to chase those payments. And so that's kind of an important factor in this. And finally, a loud amount as we already talked would be that total reimbursement. So when you add up what the plan is paying plus what the members expected to pay, you get that allowed amount, which is a usually a negotiated rate between an insurance plan and a provider. Not always, there's exceptions to every rule. But I think for most people, what they're used to seeing is an explanation of benefits or an EOB. This one I thought was pretty nice that Blue Cross in Blue Shield of Texas had posted. And basically this is trying to say what the service was, when it happened, and then that build amount is the charge from the hospital. Then there's those discounts and reductions. That's where the negotiation comes in. And then that's how you get to that allowed amount in column I. And then the health plan pays a portion of that in this case. And then in this patient's case, they had both a deductible and some coinsurance, so a proportion of those services to pay out of their pocket. So probably as if I got this in the mail, I'd be looking at that 1701 and stressing out. But there's a lot that goes behind this. And I think it's really important to keep in mind that this is probably, it could be a dozen different claims that try to get narrowed down to this service. So there's lots of different ways that the invoicing and bills are going around behind the scenes. So it's hard to kind of get this look in a claims database, but this is kind of the way that we've tried to help make it meaningful at an episodic or monthly level for a patient. And so goals, these are kind of four that I think are probably not exhaustive, but pretty comprehensive. And that is one to inform consumers. So by providing consumers the information, they can get some helpful planning in place for any upcoming medical care that they know that they need. And then it can help reveal any price differences. So if different providers are getting different levels of payment or allowed amounts for services, that might change the consumer's behavior. So they might be looking for a place that offers a lower allowed amount for a service. And piggybacking on that is the theory is it might also encourage competition. So if providers see that they're charging relatively or their allowed amounts are relatively higher than other providers that it might influence them to try to price things differently. And then finally, and this is probably one of the more controversial goals, but there is some theory that it would help with care planning. So if a provider is able to understand that the options available to the patients and include the cost burden that those options have for that out-of-pocket expense, that that might help make treatment plans that are more reasonable and reflective of the experience of that patient. On the flip side, we've got some challenges. So as I've kind of previewed this information can be really difficult to understand. And even sometimes I have a hard time understanding my EOB that comes in and I know a lot of detail, unfortunately. Also there's pretty solid consensus that just the allowed amount alone is not super meaningful without some sort of dimension of quality. So if something's really inexpensive, but not done well, that is a problem. And particularly the idea that in absence of that quality higher allowed amounts are assumed to mean that it's higher quality when that might not be the case. So if you see a $200 mattress, a $500 mattress and a $1,000 mattress, like, well, that one must be the best, but that might not be true if you don't have kind of some reviews behind backing that decision. The other problem is no matter how good you are at doing your homework in advance that you show up, all I need is my screening colonoscopy, but bang, they find three polyps. And then all of a sudden you signed up for a lot more care than you realize. So it's totally clinically appropriate to address those, but that's not necessarily what you signed up for. So it's kind of hard to anticipate. You might go in for what you expect to be a vaginal birth, but end up needing a C-section. So these expenses change. And so no matter how well that we kind of plan, it's not really possible to do it perfectly. And then as far as helping providers understand the cost implications for options to a patient is that there could be an argument that that also might bias care. So you might give differential care based on knowing what the reimbursement might be like for you as a provider. So most provider, not most, I should say, anecdotally I've heard from providers that they just want to be blind to that. They just want to be able to treat all their patients equitably without that factoring into their clinical decision-making. And I think the other thing is that a lot of these tools are not necessarily aligned with incentives for either the consumer or the providers. So even if you offer that information, nothing's kind of backing it up for people to kind of use these tools or benefit from them. And when I say difficult to understand, even if you're an expert, it can just be really challenging to figure out even what procedure you need, whether it's inpatient or outpatient, how to navigate to the actual care you're gonna get. So I think that's a really another critical component of trying to untangle this. So the first tool, the tools we'll talk about are the CMS price transparency rules. So the hospitals have to produce these annually and they need to show the actual negotiated allowed amounts by the different plan types. And there's kind of two sets of files or tools that they need to offer. One is a comprehensive machine-readable file with their bulk of everything that they do. And then they also need to provide a subset in a consumer-friendly format. And that's called the shoppable services. For health plans, that's being rolled out in three phases. So phase one started just at the beginning of last month. And that is machine-readable files for all in network-covered items and services plus the allowed amounts for out-of-network provider services. Right there, that's a pretty heavy lift, especially the out-of-network part. So they had to comply with that. In phase two, which begins in the new year, they will also need to have a consumer cost sharing tool for 500 specified items and services. I should note, and I meant to look up the citation about when this happened, but in Vermont, the health plans do offer consumer tools by law. So that part kind of has been fulfilled by Vermont law. I don't know if it's the exact same services. So they might need to extend it or adjust it, but that's something that's already been kind of available in our state. And then finally, the following January 24th, they need to expand that to be all services. So that, and I guess one other, yeah, the current tools are only available for members. I'm not sure whether or not these will be public-facing or restricted to members. So I can make a note of that and look that up. Or someone who's on can correct me where I've just spoken. All right.  And so I just went to four random facilities and got a variety of different answers. Some of them I don't think could possibly be right. So I like obviously made a mistake along the way. And it took me probably, you know, 25 minutes to gather these. So I think like there's a big burden on the consumer to pull all this together in the current format. So, but yeah, so that is just one really important aspect of this. And, you know, you can see as an example in this spreadsheet version that they were able to break out both the physician component and what the hospital component of that service would be. So in some cases that's much less clear. But yeah, that's, that was a long time to get that all together for four screenshots, but it was good humbling. And so for plan transparency, much more technical prowess is needed. So basically right now I tracked down a JSON file. So that's a type of structured data that's in a text format. And I, you know, did some digging. And here I found four different negotiated rates for this payer with different providers for a knee replacement. So you can see, you know, you would have to be kind of a data wizard to get at that. So it's definitely not ready for consumers yet, but, you know, this information could be powerful if anyone can harness it from just my observations. It looks like there's a lot more structure to what the plans are providing than the hospitals. So that might be an easier thing to try to consolidate, especially by some, you know, larger data companies, but the hospital stuff is still pretty tough to kind of get to the same comparative level. And I should say that, you know, an incredible amount of work goes into creating these files. It's not trivial for the plans to pull these together. So overall I would say the strengths of this resource is that it does provide us very detailed information by payer and provider. We see that a loud amount, which previously we really only were seeing those billed amounts or charges. And it is, you know, something that where you can see for off hospitals and eventually we'll be able to see for health plans that CMS can boss around. But the limitations as I have labored as that it does take a lot of time and significant content knowledge to try to pull those together. And the other kind of challenge is that if you are paying for a service through a plan, this tool isn't gonna tell you what your expected share is from the hospitals. It just tells you what that overall amount is. And so your insurer is the one that can really help you with that level of detail. So it's really, and then there can be a variation in how services are bundled and kind of paid for across hospitals. So at times it's really hard to really get to an Apple's comparison for some of this stuff. So that's kind of the high level overview of those tools. So the next one we're gonna take a look at is the Nash piece hospital cost tool. And I had this all set up before my computer crashed, but oh, it is still set up great. So here, I'm just gonna walk you through this set again, very high level, but essentially what they have provided is information they've harvested from something called HCRS. So cost reports are submitted and updated and put into this public use file that does get updated. So one important thing about cost reports is just to make sure that we know which things are more likely to be updated down the line versus others. But the first tab allows comparisons among hospitals. So if I selected Vermont as my state, and so here we see adjusted patient discharges by hospitals. So no surprise that the University of Vermont has the lion's share of those discharges. One important note is that this is something that's estimated. We know what the inpatient numbers are, but outpatient is just estimated based on the charge proportions. So if that number's off, it's gonna trickle down through all of this. However, the same method is used across everything. So unless we have reason to suspect a systematic difference, that might be okay, but it's just an important caveat. And then if you look at how they're measuring the net patient revenue per that adjusted discharge, you can see kind of statewide comparisons. So Vermont is showing up as 11,923 for the median. So that is in the second tier of their encoding here with Alaska being one of the more expensive at 33,459 with 22 hospitals. But I think one thing I like about this is I think it does have kind of, if you actually encode the variation that we see, it does help clean it up. I'm sorry, we're in the third tier, not the second, but yeah. And you can see that this thing is also gonna be sensitive to utilization. So if people aren't utilizing care, the expense goes down, the revenue goes down. Is that right? Yeah, yeah. Okay, sorry. And then here we see it just in a graph form for the Vermont hospitals. So one thing to keep in mind here is that low denominators are gonna make for some wonky things, which is probably part of what's going on here with Grace Cottage's result. And also the types of care they're providing. So if they're providing more intensive care, like I say rehab or something, probably that value is gonna be greater. And then they have the operating cost per adjusted patient in discharge. This is as defined in the cost reporting. So again, nobody knows what cost is. This is just one definition that is not the true expenses necessarily faced by the provider, but Medicare tries to get it in a consistent format. So we're trying to get apples to apples there. Here are the operating costs by discharge for the Vermont hospitals. And then the operating profit and loss. So this is from calendar year 2019. These numbers are likely not gonna tie to what we get in the hospital budgets just because we have a much more comprehensive set of financial indicators than the cost report offers. So this is gonna be high level policy idea of comparison. So I think it's important to remember that we're gonna have more detailed data. They also show charity costs, uninsured and bad debt as a proportion of their net patient revenue and the cost to charge ratios. So basically of what Medicare is assigning these things to how does that compare to your bill that you're getting sending out? So that is the first, oh wait, I'm sorry. The last thing that they include is what they call the commercial break-even point. So that would be what proportion of Medicare commercial payers would need to pay to get to 100% of the Medicare rate knowing that they're taking losses. So Medicare doesn't pay 100% of the Medicare rate per se. So this is also gonna be something that we wanna watch carefully in the next round because those kind of ratios are based on the RAND study that we'll talk about later. And this is in RAND version 3.0 which didn't have a lot of Vermont data. So this needed to be computed for Vermont hospitals. So I probably why I almost skipped it is like I'm not ready to look at that. I'd love to see it next year once I have our data in there. Yeah. So then you can also look at a single hospital on some of the same metrics. So I just picked Maine Medical Center. So you can see their operating costs and revenue over time, net profitability indicator. So how much are they, what's their operating loss or gain, the net profit margin, that uninsured and bad debt over time. And then they have a payer mix including that charity care and other fund sources. They have it by payer type what those operating margins are and the profit margin. And then here are the cost to charge ratios over time and that commercial break even over time. So lots of material here. And then if you wanna look at the whole state so you can compare Vermont to national it's pretty much the same measures. And then you can also, one thing I think is kind of neat is you can look at it by health system. So here if we compare the UVM health network to the Dartmouth Hitchcock Health Network you can kind of see how they are operating at that more network level versus at individual hospital level. So I think it's an interesting tool. I think it's a helpful tool but I also think it's a high level tool and that it can be the start of some questions but I wouldn't end there by any means. And talking with the people who developed it said kind of something similar. This is met high level top line comparative to help inform policy. So they were not recommending it as a direct regulatory tool by any means. It's also somewhat lagged. I'm already getting into this but yeah. As I said, standardized high level information we can see those comparisons that we walk through. They do use very transparent methodology which is nice and they have a public use data set which is helpful. But it is pretty lagged, 2019 is the most recent. I already mentioned that Rian 3.0 pricing might be a little suspect for Vermont in particular and those utilization estimates like I was saying. So since they're backing into the outpatient utilization if there's anything wonky with that that varies by hospital that could be kind of a problem. All right, I'm gonna take a drink. Rian, so we just said that these studies are actually feeding into the previous tool but essentially this is a series of reports that was initially envisioned to help employer groups learning about their payment variation between providers. So employer purchasing groups are the ones who are figuring out who the best administer benefits for their employees. Is it some administrative plan where they're self-fund? Is it a fully insured plan where they can be underwritten because they have a good risk profile? But the idea is that if they had more transparency into what's being paid they could help incentivize their employees to potentially seek different care options and or negotiate different allowed amounts with the providers. So this most recent iteration is the fourth take and it uses data from those employer groups. All payer claims databases like we sent in and then some health plans have also submitted data voluntarily and they're calculating two different things. One is a standardized price which is saying I'm gonna try to tie everything to the same unit of service and I'm gonna try to use that, get to that with using Medicare's relative weights and then figure out what is being paid on average. And then I'm also gonna see what that relative price is compared to Medicare. So if here's what the payment was for commercial, here's what it would have been for Medicare, what's the difference there? So what's that delta? What's the commercial payment relative to what Medicare would have paid? And I have the link here if you wanna look at all the gory details, but I tried to summarize it and I think that both those measures are important to keep in mind. So if you look at the right hand graph that shows our PPS hospitals, you'll see that the University of Vermont and Dartmouth actually have a very similar standardized price for their inpatient care. So the price per discharge is really close on the X axis but the relative price is really far apart. So that tells me that Medicare is likely reimbursing Dartmouth differently than UVM. That probably has to do with the regional adjustments. I think that Dartmouth might qualify for the Boston kind of bump. So I think that you can't look at the relative price alone is kind of the major thing here. But if you look at the median reimbursement per discharge in the whole study was about $20,500. So we have a few that are above that and a few that are below that for the PPS and the median reimbursement for the critical access hospitals tended to be higher than the median, but also overall there was less variability in that dataset than the PPS hospitals. And so for outpatient, we see, so price per outpatient service is gonna be a lot more services less expensive each. So we see that most of our hospitals are above the line but not too far off the 282 per outpatient service on PPS a little bit more spread for the cause but their price per service is a little bit higher as well. So the 331 for the relative price but we see more of that disparity in the relative price in between Dartmouth and UVM and the outpatient side. So yeah, so those are those studies. So it's just commercial insurance but it is give us more of a national perspective on facility differences. And I like that it does both of those calculations. So we can kind of consider those more holistically. However, whatever we see in her may be distorted by the plans we choose to participate in particular if we've over-weighted self-funded plans they tend to have a higher average allowed amount. So that might be bringing things up in this study. And we're only seeing that commercial stuff and the comparisons are at a facility level. So sometimes that's a little tricky because a facility that's doing relatively more acute care is gonna look more expensive and that's not about anything but like this type of stuff they're doing. So even if you standardize it by the relative units they're doing more acute relative units. All right, two more to go. Everybody doing okay, here we go. So our payment and cost variation work was done by Burns and Associates. And so part of what actually inspired it is getting some feedback about challenges with the RAN methodology. And one thing that immediately cropped up is like there's no assessment of costs there. We talked about how that's a very difficult thing to factor in, but we kind of blended both the hospital cost tool and this RAN thing to come up with this methodology. And so what we said is, let's look at what the cost to charge ratios are in the Medicare cost reports. Apply those at a claim line level to kind of get those relative weights and we're gonna see how things compare for what the Medicare cost should be. So those ancillary services, bill and charges to the cost is gonna give us that Medicare allowable cost. So it's saying, according to Medicare this is what that service should cost to provide. And then they look at, we also looked at what the payment variation look like and how much those payments are covering those Medicare derived costs. And so kind of the lessons we saw there is not surprisingly, this is looking just at the outpatient services that are adjusted for acuity. So the average payment for the commercial plans was quite a bit higher than the governmental payers. However, we saw much less spread in the costs. So even though costs were pretty comparable across the payer types, the reimbursements are higher for commercial. So this doesn't tell us why that is. That doesn't tell us if it should be. That just tells us kind of something we already know, but that the payment rates differ based on payer type. And we also, I think what was neat is that we also looked at how the costs, I'm sorry, how the Medicare allowable costs payments and payment to cost ratios differ based on service line. So we not only saw comparisons across facilities, but even within facilities by service line. And we can see here that imaging tended to have a higher relative payment. If we Medicare fee for services 100%, closer to 300% for that, those imaging buckets for commercial payers. And we can see that it also was interesting that for some procedures that we saw that Medicaid was actually offering the best payments for mental health and substance use services. So the strengths of this is that it did incorporate some assessment of costs in an apples to apples way and give us some insight into that variation within service category as well as across hospitals. But it only was the facility claims. So that's the thing that's easiest to be like, yes, that I know this is tied to this facility. It's only the people we have in our APCDs of Vermont residents with claims in there. And as we've really driven home today is the actual expenses to the hospitals, not the same as the cost as defined in the Medicare cost report. So even though it's like a way to get at comparative costs, it's not actually what that service costs or all the expenses behind delivering those services. And then last but not least, per statute, we built out this reimbursement variation report. And this one's quite a bit different look than the others have been. So this is saying more from say a consumer's point of view, if I have an upcoming service, what is the total cost of that episode? So I know I'm gonna have imaging, I'm gonna have maybe some aftercare. So we are pretty tight. We don't like do any like follow-up periods or anything like that, but we do include everything that happens on the day of a surgery for outpatient surgery types. And I think what this, I just wanna remind people that, there's a median reimbursement, that's the allowed amount for that service, but there's variation within that. So there's both variation between the hospitals and within the hospitals. And so it's very hard to show both those things at the same time. So just a reminder that any points you see here have these confidence intervals around them. That there's definitely a lot of variation even within the same service at the same facility. And so here, this is showing MRI of the spine. So you can see this is for all payers, what the difference in that allowed amount per episode is. And you can select for different procedures and you can also select for different time periods. So that's just a very high level and you're also able to look at it by payer type. So this shows commercial Medicare and Medicaid. So good content validity when you see Medicaid all lined up. That's what you'd wanna see if you're doing it correctly, but you can kind of see how much variation there really is between providers for this. And then if you wanna look at a specific provider, so if we wanna look at Dartmouth, we can see that point estimate as long in the variability from the fifth to the 95th percentile, as well as their payer mix. And if you wanna only look at it for certain services, you can also drill down to that. And then that full data set is here as well as some technical documentation. It will also show you the, if you want, you could just look at the member share. If you wanna be interested in the expected member share variation, you can see smaller numbers, a lot more variation. Stu, that is the last tool. It's, I think it's helpful to be able to show the full episode of care and showing that all that variation within and across facilities. We're not incorporating a specific person's benefits. So it's not very helpful for shopping purposes again, because we don't know what your specific benefits are. It's also lagged, you know, we're up to the calendar year 2020 and it's only limited to a few services at this point. And probably the most important point for regulatory purposes is just because a facility is associated with the service, doesn't mean they got all the cash for it. So like anesthesiology is kind of a classic example. They're often contracted out. So even though that's included in the cost of the episode, that doesn't mean that facility got that money. And so it's gonna be less kind of one-to-one with what the hospital or other provider revenue might look like. So to wrap it all up, all the resources are backward looking. When we're thinking about budgets, we're looking forward. So that's always an important caveat to keep in mind. And I think that all of these tools aren't gonna be a tidy fit for a hospital's financial picture. And that's okay. Like we can buttress analysis around that, but I think there's not any silver bullet here. And I just want us to be careful with some of these tools. So for instance, looking at things at that negotiated level, unit level, could distort things just because of those billing practices and other things. So it's also unwieldy to try to get it in a comprehensive uniform format as I showed you. So that's just gonna be, it can give us a sense of how things are going if we have a specific type of service question. I think it would be a good tool, but when evaluating next year's budget, not the easiest thing to translate into that. The ran payment variation, I think at a very high level can look at both that standardized price as well as the relative prices and look at some, they do have, I think two or three years of data in there so we can look at some trends. But that's all back to 2020. It's very hard to apply some of those lessons to the current environment. For our payment and cost variation. So we pulled out a lot of things because they don't really have a Medicare allowed costs or payment associated with them. So labs are kind of a notorious example. There's not an OPPS reimbursement tied to that. So we don't have that in the study and that tends to be a revenue loser sometimes for hospitals. So the other thing is that we're not seeing a lot of other important dollars such as dish, graduate medical education in some cases. And the tricky part is that we're using the would have been paid for Medicaid and not those fixed perspective payments. So as we move away from fee for service, this kind of comparison gets a little funny like when you're looking at what would have been paid. So that's one of the trade-offs I think to reform efforts. And I already kind of highlighted the challenge with the reimbursement variation report. So I don't mean to say that they're like not valuable tools. I just think that when you are trying to apply them for a hospital budget that there might be little slivers here and there we can kind of apply but we're not gonna have like a magic way to like figure out what is appropriate in this domain. I think, yeah, I think it would be a great goal for us as an organization to think about why do we care about price variation and kind of start there. And then once we know what the goals are around monitoring it, we can kind of hone in on more specific ways to measure what we're interested in. So that felt like an intense lecture. I'm sorry. I think you're on mute, Chair Mullen. Good board members have any questions or comments for Sarah? Robin, your lips are moving, but I can't hear you. She'll have to sign off and back on. I can be very brief while she signs back on. I just wanna say thank you, Sarah. It's a topic where terminology matters a lot. And people will both use the same term and think they're talking about the same thing when they're not. And it happens over and over again. We see this just talking about margins. If you have a financial officer and a surgeon in a room and ask if they understand margins, they'll both say yes but they're not thinking the same thing. And so it takes work to make sure that there's shared information, shared understanding and shared language when you do this type of work. And so thank you very much for talking us through this. I appreciate it. And Robin, are you back? I am. Can you hear me now? We can. Oh, good. Yes, my usual teams challenge. So I wanna say thank you, Sarah. I think this is helpful context to your point. Not one-to-one translatable into the budget process but I think having the different lenses provides more context, which at least for me is always helpful so that I'm seeing things in a broader and in the bigger picture as well as the minutiae of each specific hospital budget. I didn't have any specific questions, but thanks. Okay, other board questions or comments?  Every time I've tried to kind of poke around it, it's so easy to get lost. You've had some of the independent providers say that they're not being treated fairly by the commercial payers and you can kind of go on and look at some of the procedures in our variation report. And at least I think I see that. I think I see information that would confirm what they're telling me but I don't know, I'm just not sure. And I mean, when I first came on the board, there was a cardiologist, an independent cardiologist that came up to me at a meeting and said that they were getting paid peanuts for an echocardiogram, for example. And you look at your report variation report and that cardiologist is being paid 322 bucks for their echocardiogram versus the UVM medical center getting paid at the higher end of $2,166. But I've learned that you can't jump on that data and write it anywhere very far, very fast. As much as you want to help these folks. But one thought I had is, given the CMS data for the hospitals, they all have to produce a report once a year on January. And so I've spent some time poking around those websites and a few other people I know have as well. And they found that it was very difficult to use that because different technical platforms, it's just a whole bunch of variation out there. And I just wonder that for something that has to be produced once a year and there's only 14 of them in Vermont hospitals, why can't we at least assist in creating a common format for the reporting of that data that all hospitals, and we would have to work us out of the CMS, but given our relationship with them, I can't see them resisting this. So that at least once a year, that data, those 300 shoppable procedures are in a format that allow not, maybe not so much patient facing use, but public policy use. Yeah, it's just overwhelming. And if there's somebody out there that can create a simple path through that and how they used it, it would be helpful. So that was just one thought I had is once a year, there's 14 of them, why can't the Green Mountain Care Board save the hospitals? Here's the format you're used because obviously all Vermont hospitals have submitted two or three of those reports by now because this law is two or three years old. They've all submitted them and none of them have been rejected or fined or anything. So it seems that what they're submitting, even though it's a variation, CMS isn't complaining. So if we could get them on the same platform, that might be helpful. But there again, I'm sure there's a hundred reasons why it wouldn't be helpful, but that's my simple thought about that one. So thank you for this. I mean, it's, I've learned to embrace your ambivalence about making any conclusion about what you think you see because what you think you see might not be what you really see or so. So thank you. Thanks, Tom. Other board member comments or questions? If not, I'm gonna go to public comment. Does any member of the public wish to offer comment on the price transparency presentation? You can either raise your hand or if you're on the phone, just speak up. And the first person I'm gonna call on is Eric Schulteis. Eric. Great. That was a wonderful presentation. Thank you so much, Sarah. I think when I'm looking at how much the board's capacity to look at data has improved and I'm thinking about what the next step is, it really is in thinking about continuous improvement. It really is moving beyond data and information to knowledge and trying to understand what is the cause of what we're seeing. So data informed policy shouldn't just be about looking at numbers, but actually using the data to draw hypotheses about why things happen the way we do. And I mean, I think something Sarah said, it just shows us what is. If I'm thinking about some work I did for the Cambridge Housing Authority that I think that illustrates this point is we did mapping over a decade and a half of section eight voucher flows, usage flows. And in the end of the day, all that I told us was that there's this movement happening outside of Cambridge. And I think the knock on part of that was to try to understand why that was happening. And that involved talking with clients, it involved talking with realtors, it involved talking with the universities. And what we ended up finding was that the universities were selling or leasing property to large companies and it was displacing that. And the universities weren't like kind of building affordable housing for residents of Cambridge. And so I think for us, it meant that if we had just tried to increase the fair market rents that HUD allowed, so allow clients to lease off, that really wouldn't have solved the underlying issue, which was what the universities were doing and how they saw their commitment to the community. And they weren't thinking about this, they weren't aware of this outflow of Cambridge residents. So I think that in the end, data informed policy is trying to move just beyond charts and data and draw inferences from that data about why things are. And the hope for that is that then when you have policy interventions, it's your best guess about what that interface, based on your idea of causation, what that intervention will do. And I think that in the end of the day is what's most important to having effective policy. Thank you. Thanks, Eric. Next I'm gonna turn to Walter Carpenter. Thanks, Kevin. I guess this is your last meeting, huh? Wow, I came in the last row. Your last row, God, I wore out, I wore out three board chairs. In any case, my comment is, I'll back up everybody who said, praise Sarah for her presentation. And I just back up Eric and what he said. I also wanna say that this presentation argue is a great argument for simplicity. As a patient, to try to get to the bottom of this would be virtually impossible as Tom said and Tom's experience with this, as he says that, can you imagine what it was like for someone who was uninitiated and as to suddenly look at these facts, figures, statistics, et cetera, and try to compare them? And it's just, it's nuts. So I was wondering if there is a way to simplify the whole thing and put it down into, here's what's this charge, et cetera, et cetera. That's all. Thank you, Walter. Next, I'm gonna turn to Ham Davis. Thank you, Kim. I may basically have questions if you could ask, I could ask through you. One of the questions I have and I'd like to ask Sarah is my sense is we're not rigorous at all, but that is that as we went through all the work by the consultants and the blizzard of stuff that's come in on all of these issues over the last couple of years, there's been some of the data has been hospital only and not doctors in other words. And so what I'd like, my first question to Sarah is, can you just tell us to what extent is the data that you're providing when you're using the word provider? Is any of it affected by being, including only hospitals and not doctors? Can you just tell me whether that's a problem or not? It depends. Some of these resources would include the professional claims. Most of them don't.  Really? Yeah, it's a huge issue. And when you say what doctors are getting, like do we mean that can look a lot different if it's like an independent provider or a salaried provider at a hospital, right? Like what's the most meaningful metric there? I don't know. Thank you. I just think we need to start figuring out a way to get the, when we get the cost of some, you go to, if you get a medical procedure, you have to pay the doctor, but you also have to pay the hospital. If you only know, and the real driver is not the hospital, the driver is the doctor, the real decision baker is the doctor. No hospital out there, no hospital administrator is deciding whether to operate on the left knee or operate on the right knee. It's all the doctors. The second question I'd just like to ask, Sarah, and I know this is maybe just a fantasy, but is there any way in your judgment, Sarah, to start building in volume into these, volume into these judgments. If you get a service, some service, a knee replacement, you guys gonna cost X at one hospital or Y at the next hospital and so forth, and you're gonna see the difference in cost of the provider. The whole purpose of the Greenmont of the Dartmouth Health Atlas, and which is considered, I think, a highly reliable piece of analytical Bible, in fact, is that if you could have a hospital with very low cost for discharge, okay, but if it was doing twice as many discharges, then the cost for the low price place would be the much higher cost place. Can you tell me what you think about this? Is there a way to start moving towards the volume-based thing, which in point of fact is the whole goal of healthcare reform? Yeah, the reimbursement variation tool does include the number of procedures. So that is there. It's not directly visualized, but yeah, I think the long game here is once we come to some goals in the hospital budget process, we can do a better job of building the monitoring tools to support them. And the hospital discharge data set was built to support the hospital budget process that's drifted over time. So I'd love to see that kind of come back into fashion and yeah, figure out what these key metrics are and do a better job of kind of simplifying them to an earlier comment. Kevin, that's all. Thank you, sir. Thanks, Ham. Is there any other public comment? Hearing none. Thank you, Sarah. It was both enlightening and frustrating at the same time. So thank you. My biography title. So with that, is there any old business to come before the board? Hearing none. Is there any new business to come before the board? Hearing none. Is there a motion to adjourn? So moved. Second. It's been moved and seconded to adjourn. All those in favor of the motion, please signify by saying aye. Aye. Aye. Any opposed, please signify by saying nay. Nay. Nay. Oh. If we don't adjourn, you can't leave, Kevin. I still have another day, Robin. So I guess I can wait you out for a day. Let's try that again. All those in favor of the motion, please signify by saying aye. Aye. Any opposed, please signify by saying nay. Nay. Thank you everyone, have a great rest of the day. Thank you, Kevin, for all you do. Thanks. Thank you, Kevin. Bye.


From the vault. So what we're gonna do is we're gonna play the videos of some floppy work that we've been doing and also We have you want to just Give people a reason to stick around you have got your little game. Yeah, I'll show a little gamer Yeah, so yeah, so we have the the the the famous tiniest playable zoom a doom playing device Open let's open source one. Yeah, maybe maybe not okay So I'm gonna play the the floppy videos first and then I'm gonna play the latest thing that we did with the Doom player. So here we go What is this? I'm finishing up my last test for the cutie pie ESP 32 you get the text backwards the Pico Board that I designed and I've been working a lot with over the weekend. I'm doing some power tests This is I did functional tests over here By having it in this little cutie pie board so you can emulate NES games But for some people they really want low power functionality So I want to make sure I've got that all fixed up That's really hard to fix low power mistakes after the board's been sent out. So here is my ppk from Nordic and you can see You know normal functionality. It's about 40 milliamps. And then this is light sleep, so it's about 2.4 Milliams 2 to 3 milliamps and then for deep sleep when I turn off the neopixel I get down to about 70 microamps, which is great because the chip is about 50 microamps and the regulator is also about 20 So total about 70 Early data, what is this? Okay? I've had a lot of success over this weekend porting retro go over to the ESP 32 cutie pie That's this is gonna ESP 32 on the back of it a little bit like this.  Oh wait. Sorry. That's a Different game new game And I've got even audio working out the headphones so you can actually start to play these games and test them out So very more is one of my favorite old-school rpgs. It's by epics pre-final fantasy But this is working and I only made one little mistake Audio is coming out of A3. It isn't coming out of A0, but I'll fix that on the next board wrap Okay, so nefrendo works fine on the ESP 32 cutie pie with this little board. Um, and I thought well, maybe let's do Um Game boy next so this is new boy go. Let's start a new game And I remember playing this I remember getting a game boy when I was very young It was like a birthday present. Maybe it was like 12. I don't know But uh, I played this game all the time. That's in tetris. I didn't have a game boy color They're just the uh game boy classic still. Um, it's a great little game and uh, I don't know I can't wait to like finish this. I don't think I actually finished it when I was a kid but um, all right, so, uh Game boy me later works the Nintendo emulator are gonna try a couple more emulators next Early later. What is this? Okay, so I'm taking a pause from the floppy stuff, but for a good reason. Um, this is an o dory go This is a really cool Like portable, you know game boy like emulator that had an esp 32 rover in it And it could load cards off an sd card and like there's a bunch of emulators written for it like On nefrendo and yes, uh new boy game boy Sega etc etc Um, I still have one of these are not made anymore, unfortunately, but this was really cool Actually was a really good little emulator, but what's nice is that I wanted to try to port um The emulator itself to the esp 32 pika so I can make like a really cute Little gaming things is my prototype and you can see I got the tft initialization like not quite right, but I do have tft on there And on the back is the cutie pie esp 32 and sd card And some circuitry battery charging and all that so, uh, it's slowly but surely coming together first sign of life Really dude. What is this? Uh, one more emulator that works in retro go is sega master systems, which means, um, you know, somebody was asking Can you play sonic and? Yes, you can get all the rings Oh, no Not very good scheme, uh The other thing uh, somebody was asking was uh, is there cheats for doom and the answer is Um, you know, you normally you would like type in stuff on your keyboard But with this You can go into the options menu And under cheats you can, you know, do the chainsaw and vulnerability 200 health get all the keys And all that uh makes the game lots of fun because you can, uh Give all the weapons from the start Come on Great for playing doom on the go with all your favorite weapons Early, dude. What is this? This is a, uh, three and a half inch hd floppy disk that has been formatted as a mac 800k I did that by tricking it by taping over the hole over here Um, and I put this in the power book 180 and formatted it. Thank you for the power book 180 Uh, it's 800k because 800k floppies are kind of interesting and I've been trying to read this on some of a standard PC sony or panasonic three and a half inch, uh floppy disk drive But what's interesting is it doesn't really work here get it with, um flux engine you can see, um The dots are sectors that read fine and you see like as you get to, um higher and higher tracks Um, you know the sectors don't show up and it's like they're okay for like the first few tracks But then they disappear. Well, the reason for that is actually kind of interesting mac 800k floppies are weird um Each track actually has a different number of sectors and the pulse widths change. So, um on the outer track Which is actually track zero the pulses are very short and as they get closer and closer to the center near the hub um, the pulses get wider because you want more, um magnetic media to pass under the head and, um for mfm floppies Which is what normal ibm floppies are for floppy disks. The track widths, um, sorry the bit widths don't change. They're like two four six Um microseconds and they're the same no matter what track they're on whether they're on Track zero all the way out here or track 80 all the way in here So if you're trying to read mac format floppies the outer tracks will read just fine Because they use the short bit widths, but as the bit widths get larger in time But stay the same in physical space these disk drives have trouble reading them So we might have to do some special math to get mac 800k floppies To work with this could also be the disk get itself more to come And that's our secret. Yeah. All right. We want to get some questions going


Welcome to The Anxious Morning, where each weekday morning we take a look at ideas, concepts, and lessons designed to help you understand and overcome your anxiety. For more information, visit us at theanxiousmorning.com. Every now and then, I'll write an edition of The Anxious Morning that shines a light on what my life and mental health looks like post-recovery. Today, I want to talk about the importance of maintaining good habits. The lessons that I learned during my recovery and the lessons that life continues to teach me tell me that it is important for me to maintain good habits with respect to my physical and mental well-being. For many years, I made the mistake of believing that I am a machine and that the usual rules don't apply to me. Well, guess what? While we all have different performance levels, even those of us with machine-like performance levels do need to take care of ourselves. Now, why didn't somebody tell me that? Well, actually, lots of people told me that. I was just too stubborn to listen to them. When it comes to good habits in my life, there are a few that I like to think of as my foundation. They include regular meditation practice, mindfulness practice, making an effort to not race through every day, taking time to relax and unwind, and moving my body regularly. When I commit to these habits and to taking steps to practice them and maintain them, I find that my performance level remains pretty high. My stress level doesn't get out of control and that I am generally more satisfied and happy with the work I'm doing and with my life in general. When I allow myself to go off the rails, which does happen sometimes, I can feel the change within days. I get moodier and more withdrawn. I get short tempered. I start to feel physically sluggish and more tired and my focus skills begin to suffer. It's no fun. Maintaining these good habits is effort. It doesn't happen automatically. I've had to accept more routine and planning in my life than I'd otherwise like, but this leads to good things. I put my good habit things on my calendar and in my task manager. Every morning when I wake up, I know that I have meditating, moving, reading and learning, and relaxing to do on my list as real things that must be done. Now, sometimes that seems silly, but the reminders are good for me. They help keep me connected to good physical and mental health, which makes my life better on many, many fronts. So when people ask me if I struggle with anxiety now, the answer is absolutely no, I do not struggle with anxiety like I used to. But life after recovery is not automatically sunshine and rainbows all the time, because it's not like that for any human being. We all deserve good mental health, but we also have to work at that. So even after recovery, developing and maintaining healthy habits that tend to your body and mind is just a good idea for more reasons than I can list. What good habits can you see for yourself beyond recovery? If you're enjoying the anxious morning and you'd like to get a copy of the podcast delivered into your email inbox every morning, visit the anxious morning dot email and subscribe to the newsletter. If you're listening on Apple or iTunes, take a second and leave a five star rating, maybe write a small review, it really helps me out. And finally, if you find my work useful and you'd like to help keep it free of advertising and sponsorships, you can see all the ways to support the work at the anxious truth dot com slash support. Thanks so much.


QuickBooks Online 2024, vertical analysis, profit and loss, PNL or income statement. Get ready and relax because it's so easy using QuickBooks Online, you'd think it'd be a crime. But it's not unless you're doing bookkeeping for like porch pirates or something. But anyways, let's get into it. Here we are online in our browser searching for QuickBooks Online test drive looking for the result that has Intuit.com in the URL, Intuit being the owner of QuickBooks, selecting the United States version of the software and verifying that we're not a robot. Opening up the major financial statement reports like we do every time the reports are on the left hand side, we're in the favorites, we're going to be right clicking on that balance sheet, open link in new tab, right click on the profit and loss, open link in new tab. Let's go to that middle tab that we just opened, close up the hamburger and change the rank.