antibiotic therapy directed against multiresistent bacteria is a significant cost driver in clinical medicine . due to various reasons ,
the costs of new antibiotics are comparatively high and some multiresistant bacteria can or should only be treated with new antibiotics .
moreover , most complicated bacterial infections requiring long treatment durations occur in the hospital setting , often in intensive care units .
it is a well established notion that prolonged length of stay in the hospital ( los ) , the time spent in intensive care units ( icu days ) and the hours of mechanical ventilation ( hmv ) are the main cost drivers in this setting [ 1 - 6 ] .
the evaluation of the economical effectiveness of pharmacological therapies is gaining more and more importance .
while most of these pharmacoeconomical analyses are intended to show effectiveness of a new drug versus the current standard - of - care from a healthcare system perspective ( cost - effectiveness studies , prospective modelling , using quality - adjusted - lifeyears ( qualys ) and other parameters ) , these studies do not necessarily answer the budget - related questions of clinicians or even the administrators in a hospital [ 7 - 10 ] .
finally , in most developed countries so - called diagnosis - related groups ( drg ) are used for hospital budgeting , reimbursement or resource allocation .
drgs are payment groups that determine the reimbursement for a certain type of patient group that was found to be economically homogenous .
for example , the germany system provides drg - directed payment for respiratory tract infections with or without " complications or comorbidities ( cc ) " .
the following table gives an overview of drgs for respiratory tract infections , their cost - weights , their payment ( assuming a base price for cost weight 1.0 of  2,900.- ) and the related length of stay ( los ) in hospital , see table 1 .
overview of drg e77 - respiratory tract infections drgs are common instruments for hospital reimbursement or budget allocation in most developed countries in the world .
nearly every country in europe has drg - systems in use . without drilling too deep into the complicated german drg system and translating all the drg - denominators , we point out , that drg payments for one particular set of conditions - like respiratory tract infections - vary according to various cost - modifiers . one of these factors is the occurence of multiresistent bacteria ( see bold letters in the table ) . however , the payment for a given drg is fixed at a certain amount .
the german costing study includes annually calculated average costs in a matrix of cost types ( such as staff , pharmaceuticals , etc . ) and cost centers ( such as normal ward , or , icu , etc . ) .
this results in a costing matrix with up to 100 so - called cost modules .
moreover a national los " benchmark " is available , and these data are published in the internet ( for each drg ) . after the introduction of drgs , optimizing
the los has turned out as an important lever to achieve higher profitability . yet , this notion has not yet been fully acknowledged by everyone in the medical community . as an example , table 2 shows the cost matrix of e77b - respiratory infections with complex diagnosis or catastrophic cc , w / o complex intensive care for children , with treatment of multiresistent bacteria , hospital stay longer than 72 hrs cost matrix for drg e77b based on the known " limits " that must be observed to avoid losing money in drg - reimbursed treatments , analyses can be done on an individual hospital 's data to determine whether the current treatment strategies in a hospital lead to a sustainable balance of cost and medical need . coming from this idea ,
the development of a drg - based approach to the analysis of infections and the prove - of - concept were the major questions to be dealt with in this publication .
as in drg - based payment systems , the coding of diagnoses as primary ( the reason why the patient got admission to the hospital ) and secondary ( relevant complications and comorbidities that caused resource consumption ) diagnoses is the cornerstone of finding the correct drg , we assumed that the coding quality in terms of completeness and accuracy pretty well reflects the clinically relevant situation , especially in case of infections .
we developed an algorithm that contains over 100 icd - codes representing infections and/or bacterial pathogens .
hospital - acquired versus community - acquired infections were assumed to be represented by the assignment as " primary " ( or main ) diagnosis or " secondary " diagnosis .
moreover , we tried to rule out coding errors such as the implausible use of the same icd code as primary and secondary diagnosis .
hospital acquired pneumonias ( hap ) may be indicated by a special icd - code ( u69.00 ! ) used to distinguish between community acquired pneumonia ( cap ) and hap in the german system . using the minimal basic dataset ( mbds ) of a country - in germany
21-data - for one hospital or a set of hospitals , it is possible to " decode " infections from the drg data .
table 3 displays the basic data model used to retrieve information on infections and bacterial pathogens from coded icd-10 data . entity relationship model of the " decoding infections " database as in some cases , bacterial pathogens are part of the icd - code of the infections , counting infections and the bacteria involved respect this fact by listing some icd - codes as infections and as bacteria . see table 4 for an example .
association of bacterial pathogens and icd - codes using this methodology , we were able to decode infections from routine drg data .
after implementation of these data in a business information warehouse ( bi ) software , it is now possible to answer the following questions : - which infections caused by which bacteria occur in the hospitals ? - which los is associated to which infection and does it imply a risk of losing reimbursement for the hospital ? - which drgs are the ones most likely impacted by infections ? once identified , the drgs with a high number of infections or those patients that cause the highest loss in drg - reimbursement due to infections may be further analyzed . in peer reviews , the antibiotic therapy strategy for each case may be compared against the expected cost average in the respective drg , the actual cost and the potential cost outcome achieved by using a different therapeutic strategy .
differences in length of stay that cause inefficiency can be detected and assigned to various types of infections .
table 5 shows an overview of certain infections and the los of patients having these infections compared to the los of patients being in the hospital without an infection .
los with and without infections without infections : 7,3 days ( green block line ) with hospital acquired pneumonia ( hap ) 21 - 28 days ( pink line ) if the los is higher with an infection , this does not necessarily imply that there is an economic loss for the hospital , as expensive cases usually also entail more revenue . by knowing that los is the key cost driver in a drg - based system
, an analysis can be performed how many patients meet the average los ( alos ) of the drg - as defined by the national benchmark - and how many patients stay longer .
those patients staying longer are the patients that cost more than the hospital is reimbursed for . tables 6 and 7 display los curves ( absolute = red curve , cumulative = brown curve ) for 1 ) all patients in a hospital and 2 ) patients having a postoperative infection such as wound infections , peritonitis or other or - related infections : los for all patients of a hospital - good result : 76% of all patients can be discharged before reaching the alos ( mvd in the figure ) los distribution for patients with postoperative infections although the national alos of these drgs is 21.9 days ( indicating that the drg - reimbursement is higher than the hospital average ) only 50% of these patients can be discharged before alos .
that means half of the patients cost presumably more than the hospital is being reimbursed for . using individual patient cases for analysis ,
the individual drg may be used and the actual antibiotic therapy strategy may be compared versus an optimum setting .
quite often it is possible to show that a state - of - the art therapy causes less cost .
table 8 shows a case simulation for a given drg - result ( a13e - mechanical ventilation 95 - 250 hrs ) . by examining the medical record ,
the reviewers found that by optimizing antibiotic therapy ( i.e. starting the eventually effective therapy with tigecycline 3 days earlier ) the icu - stay and the los could have been 2 days shorter .
moreover , by starting the adequate therapy earlier , three days of ineffective and expensive therapy ( in this case meropenem plus ciprofloxacin ) could have been avoided . in the end
, the hospital would have had small gain in this drg rather than a loss as in the original situation .
comparing national standard cost ( section " inek - daten " in the picture ) with actual cost ( section " ist - situation " ) shows that the hospital spends more than it is reimbursed ( -1,881.23  ) . by simulating the case with the optimized therapy option ( section " variante " in the picture ) the economic result shows a surplus of 1,223.10.
drg and individual case - based simulation of optimized antibiotic therapy strategies the findings of other studies and the results from the data easily can be reproduced also in the results of individual case reviews .
the main cost drivers are : - length of stay in hospital  possible reason : delayed start of effective antibiotic therapy - complications related to antibiotic therapy  most frequent : renal failure - use of inadequate antibiotics that turn out to be in effective - prolonged duration of mechanical ventilations
decoding infections from drg routine data is feasible , comparatively easy and can be done with little effort of time and expenses as the data are easily available for each hospital .
validations in several hospitals were done by using the results and reviewing selected medical records to verify whether the " decoded " infection was actually mentioned in the record .
very little variances were found to be due to coding variations . while germany has coding standards for diagnoses and procedures , errors may still occur .
the actual accuracy of the coding is very good . according to the medical services of the statuary health insurance in germany ( mds ) 11% of all hospital drg reimbursements
are claimed to be wrong and in 40% of the claims actual errors are found .
that means that nearly 96% of the coding is correct , as no claim is issued or no error is found .
it is clearly possible to identify cases that are more expensive than the drg system recommends . in many of these cases
, sound medical reasons caused the extended los , but there is a substantial part of the reviewed cases that suggest opportunities to optimize the antibiotic therapy strategy .
analysis on the basis of drg routine data is an easy way to " decode " infections in a hospital setting and directly connect them to economic results .
establishing a peer review of the medical records of cases producing financial loss may identify opportunities to optimize treatment strategies .
los , number of complications , icu - days and hours of mechanical ventilation are good endpoints to be used in the assessment of the economical effects of individual antibiotics .
alos : average length of stay in a given drg , basis for determining whether a patient causes more costs than reimbursement ; cap : community acquired pneumonia ; cc : complications and comorbidities , conditions ( like secondary diagnoses ) that cause higher resource consumption ; drg : diagnoses related groups , systems to classify patients based on their resource consumptions ; hap : hospital acquired pneumonia ; hmv : hours of mechanical ventilation ; icu - days : treatment days on an intensive care unit ; los : length of stay in hospital ; mds : medical services of the statuary health insurance in germany ; qaly : quality adjusted life years ; w : with ; w / o : without .
 the cost of treatments especially in conditions where multiresistant bacteria are involved are a major issue in times where in most developed countries in the world payment systems based on diagnoses - related - groups ( drg ) are in place . 
 there is great evidence that especially the length of stay in hospital ( los ) , the time in the intensive care unit ( icu - days ) and the hours of mechanical ventilation ( hmv ) are major cost drivers.while established methods of pharmacoeconomical analyses focus on the efficiency of drugs from healthcare system perspective , these data are often not sufficient for improving treatment strategies in a given hospital context.we developed a system that allows the analysis of patients with severe infections on the basis of routine data that is also used for reimbursement . 
 these data contain a lot of information concerning the clinical conditions . by using the icd - coding we developed an algorithm which allows the detection of patients with infections and gives information on the potential financial outcome of these patients . by using the analysis it is possible to identify subsets of infections and the patient records that had a potentially negative drg - result , i.e. the costs are higher than the reimbursement . 
 when identified the patient records undergo a peer review , where the clinical situation and the antibiotic therapy are reviewed by medical experts . in case simulations it is possible to find out if a different therapeutic approach , e.g. by different choices in initial ( empirical ) antibiotic treatment would have caused other outcomes.data driven analyses together with peer reviews of patient records are a useful tool to examine antibiotic treatment strategies and to establish changes that again can be reviewed on a regular basis . doing this a continous improvement process 
 can be established in hospitals which can lead to a better balance of clinical and economical outcomes in patients with severe infections . 
 moreover these analyses are helpful in assessing the literature on economical benefits of new therapies . 
Introduction
Materials and methods
Results
Discussion
Conclusions
Abbreviations