# Arnar Berþórsson BA Kristlaug H. Jónasdóttir BS, MSc

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Recommendations on Minimum Data Recording Requirements in Hospitals from the Directorate of Health in Iceland: Is it possible to use Hospital Patient Registry data to decrease the cost of outliers. Arnar Berrsson BA Kristlaug H. Jnasdttir BS, MSc. Landsptali University Hospital (LSH). - PowerPoint PPT PresentationTRANSCRIPT

<ul><li><p>Recommendations on Minimum Data Recording Requirements in Hospitals from the Directorate of Health in Iceland:Is it possible to use Hospital Patient Registry data to decrease the cost of outliersArnar Berrsson BAKristlaug H. Jnasdttir BS, MSc</p></li><li><p>Landsptali University Hospital (LSH)</p></li><li><p>Prospective Payment Systems (PPS) and Diagnosis Related Groups (DRG)Fixed payment per discharge.Payment is the same for all patients within each DRG group.Patients within each DRG group should show homogeneity in clinical conditions as well as in cost.Payment for DRG groups is based on average costs for patient within the group. Patients grouped based on:Principle diagnosis ICD-10Secondary diagnosis ICD-10Procedures and imaging examination NCSP+ Length of stayAgeGenderType of dischargeDRG weight: mean cost in each DRG divided by total mean cost in all DRGs.</p></li><li><p>OutliersAn observation that is numerically distant from the rest of the data.In most large samples of data, some data points will be further away from the sample mean than what is deemed reasonableThey can occur by chance, but they can also be an indicator of either measurement- or coding errors or that the data has a heavy-tailed distribution.In health care reimbursement, especially in PPS, outliers are those patients that require an unusually long hospital stay or whose stay generates unusually high costs.</p></li><li><p>Hypothesisp measures the probability that a patient will become an outlier.T0 :Following model, based on Guidelines from the Directorate of Health for minimal registration requirements for patient information, can be used as an indicator for a patients probability of becoming an outlier.</p></li><li><p>Calculation of outliersOutliers are admissions that exceed a certain cost limits calculated within each DRG group, see formula below.</p><p>Outlieri = Q3i + k *(Q3i Q1i)k = (P95 Q3) / (Q3 Q1)Where Q1 is 25th percentile, Q3 is 75th percentile and k is a constant that set the outlier limit to 5 percent. P95 is 95th percentile.</p></li><li><p>MethodologyResearch design: Non-experimental analytic analysis.Sample: Discharges from all wards within LSH except:Long term Geriatric wardsLong term Psychiatric wardsRehabilitation wardsPalliative care wardHealthy newbornsSample criteria: Discharges in the period 1. Jan 31. Des 2008 (n=21.912)Cases classified into DRG groupsDRG groups 30 cases (196 DRG groups)Data analysis: Logistic regression (stepwise method)</p></li><li><p>MethodologyDependent variable: Outlier=1, Non Outlier=0Independent variables : Gender, 1=male, 2=female Age, children 18, adults 19 to 69, elderly 70Number of ICD-10, (International Classification of Deceases) codes, (Transformed to ln(x) to correct skewness) Number of NCSP+ codes, (Nordic Classification of Surgical Procedures), (Transformed to ln(x) to correct skewness) Types of admissions, acute =1, non acute =0Types of discharges, home=1, died=2, other=3Length of stay, (LOS) (Transformed to ln(x) to correct skewness) </p></li><li><p>Methodology: Sample</p></li><li><p>Sample</p></li><li><p>MethodologyLogistic regression</p><p>predict the probability of Y occorrung given known values of predicting variables</p></li><li><p>Result</p></li><li><p>DiscussionsWhy is it that with increasing number of registered diagnosis the probability of a patient becoming an outlier decreases??Children (0-17) are more likely to become outliers than 18-69 years oldBut older patients (70+) are less likely to become a outlier than 18-69 years old.Death, mortality and length of stay provide strong evidence of who become an outliers.Patient that are discharged to nursing homes, other hospitals and institutes are more likely to become an outlier.</p></li><li><p>Limitation</p><p>DRG groups with fewer than 30 discharges were ignored.Cost is partly distributed by Length of stay, does this cause problem for the assumption to the model?We could not use Marital StatusDistinguish between Discharges to other specialitis and to other institutions.</p></li><li><p>Use of the result</p><p>The purpose is not to decrease outliersThe purpose is to influence the factors that cause the patient to be a outlier.According to this study, outliers are 7 times more expensive than average patient in the same DRG group.</p></li><li><p>Further studies and ideasEffect of marital status and discharge modeConnection between number of registered diagnosis and outliers within DRG groupAdd other relevant variables to the model such as Acuity, re-admission, waiting list, chronic diseases, test results.Limit the sample to smaller groups such as single DRG groups or MDC groups or speciality.Effect of quality of coding and homogeneity of DRG groups.</p></li><li><p>Result I</p><p>Good day, my name is Arnar BergrssonI come from Iceland, landsptali university hospital. The name of my study is.</p><p>This is a study that I and one of my college did at the hospital.What we wanted to know is ifthe variables in our datawharehouse could be used to characterize outliers in our DRG and costing system.We decided to narrow the our question and use variables that the Icelandic directorate of health requires hospitals to register.</p><p>???But we did not have access to all these variables in our databases because some of them are wrong.*Here is some key statist from Landsptali from the year 2008, but we used data from that year in our studyLandsptali is by far the largest hospitali in Iceland and the only university and high tech hospital. It serves the whole country as such. Skoa glruIn 2008 third of the total population of Iceland received service from LSH.</p><p>?????Outlier is average seven times more expensive than average patient from the same DRG group.*Here is a little about Prospective Payment Systems like DRG</p><p>A Prospective Payment System (PPS) is a method of reimbursement based on a predetermined, fixed amount. The payment amount for a particular service is derived based on the classification system of that service (for example, DRGs for inpatient hospital services).</p><p>Some patients are more expensive, others less expensive but they all recive the same fixed payment per discharge.glra*Aoutlier is an observation that is numerically distant from the rest of the data.</p><p>In the case of normally distributed data, roughly 1 in 22 observations will differ by twice the standard deviation or more from the mean, and 1 in 370 will deviate by three times the standard deviation also from the mean</p><p>Lesa glruna</p><p>Later I will discuss how we calculate outliers.*Here you can see our hypothesis The null hypothesis is </p><p>I will go over the dependent variable and indipendent variables.*Outliers are admissions that exceed a certain cost limits calculated within each DRG groupThere are many ways to calculate outliers, one can use parametric other is to use non parametric methods, in parametric methods the most comon is to multiply mean with standard deviation, for example mean multiplied with 2,5 standard diviation.In this study we used interquartile range.</p><p>K is a konstant that we finde out by using the lower formula</p><p>You can see if the distribution is heavily skewed to the right k will be larger because the difference between P95 Q3 will grow larger</p><p>And if k grow large then the limit of outliers will be higer.*We used non experimental analytic design.We used discharges from Landsptali in the year 2008.We excluded long term Geriatric and Psychiatric patients as well as Rehabilitation patientsWe also excluded palliative care patients and healthy newborns.And we use Logistic regression model.Backward stepwise method*The dependent variable tells us wheter the individual is an outlier or not. The independent variables are gender, age wich is classified to chldren 17 years and younger, adults18-69 year old and older people 70 years and older.We used number of ICD diagnosis and NCSP proseadurs.And the last variable is LOSWe used log transformation for number of diagnosis, procedures and length of stay.Types of admission and devided into acute vs non acute.Types of dischard wich is cattegorized into home, died and othere wich includes those who are transferred to other specialties within the hospital and also those who are discharged to other institutions like nursing home and other hospitals.A few variable had to be log transform to get appropriate distribution for the logistic model. They are number of icd, number of ncsp and los*Here you can see the sample. We had in total 21.912 discharges thereof 3,2% outliers.Male where 42% and Female 58% of this discharges. 3,5% of males where outliers but 3,0 of Females. Females are in majority probably due to births.Almost 80% were admitted acute, 3,7% of them were outliers.20,1 % came non acute and 2,4% of them were outliers.Those who were discharged to Nursing-homes, other hospitals and other institutes where 7,6% of the total sample and thereof 14,8% outliers. 1,6% died and 14,3% of them where outliers, 90,8% discharged Home 2,5% where outliers.</p><p>Of most interest here is high proportion of outliers among those discharged to other speciality, hospital or other institution. And also among those who die. *Here you can see average age, length of stay, number of diagnosis and procedures in the sample as well as in outliers.You can see that outliers are older, stay longar heve more diagnosis and procedures in the whole sample.*Here just a little bit about logistic regressionHere is the functione is the base of logarithmz in this formula look like normal simple or multiple regression, and is combined of constant, pridictive variables and an error termThe difference between multeple reggression model and logistic model is that in stead of predicting the value of a outcome varible (z) from a predictor variables we predict the probability of z occorrung given known values of predicting variables. So in this study by a given situation the change that an individual vill become a outlier increas or decrease by a certain amount</p><p>The interpretation is as an estimate of the odds ratio between z and a pridictive variable when all other values in the model are hold fixed.So we putt for example nine predictive variable into the model and adjust for the effect of them on each other*% reduction in the odds of % increase in the odds of </p><p>Gender does not affect whether a patient will become an outlier so it was thrown out of the model in our stepwise methodThe interpretation is then ef someone is hospitalized acute then there are 77% more change that he will become an outlier than if he would be hospitalizend non acute all while other variable in the model are hold fixedPatients, admitted acute are more likely to become an outlier than those that are non-acutely admitted (Odds 1,77; sig. 0,000).77% increas in riskIncrease in length of stay (LOS) increases the probability that a patient will become an outlier (Odds 1,94; sig. 0,000).Increase in number of medical diagnosis (ICD-10) decreases the probability that a patient will become an outlier ( -0,36; sig. 0,000).Increase in number of therapeutic procedures (NCSP+) increases in probability that a patient will become an outlier ( 1,2; 0,000).Patients that die (MORS) are more likely to become an outlier than those that are discharged home. (Odds 3,75; sig. 0,000).Patients that are discharged to nursing-homes, other hospitals and other institutes are more likely to become an outlier than those that are discharged home. (Odds 1,58; sig. 0,000).Patients that are younger than 18 years and are more likely than those that are 19-69 years old to become an outlier. (Odds 2,18; sig. 0,000 ).Patients that are 70 years and older are are more likely than those that are 19-69 years old to become an outlier. (Odds 0,74; sig. 0,004 ).</p><p>*Increased number of medical diagnosis per patient decreases the probability that a patient will become an outlier. This was an unexpected result. </p><p>A possible explaination might be undrcoding of diagnossi, perhaps patients with multiple chronic diseases and therefore often many ICD codes have many but short admissions. Little correlation between acute admissions and number of ICD codes. Swedish study implied that after going over and recoding some admissions number of outlier decreased by 25%.</p><p>Children (0-17) are more likely to become outliers than 18-69 years oldBut older patients (70+) are less likely to become a outlier than 18-69 years old.</p><p>Other way to put this is 18-69 year old people are less likely to become a outlier than other</p><p>Patients that die are likely to become outliers than those that are discharged home. Researches have pointed out that the last weeks before death are, patients consumes Healthcare Quarterly,12(3)2009:50-58 Costs of End-of-Life Care: Findings from the Province of Saskatchewan Marcus J. Hollander (62% more expensive than average patients)</p><p>Patients that are discharged to nursing-homes, other hospitals and institutes are more likely to become an outlier, (includes also transfer from one medical speciality to another). Why: Bottleneck Is the patient-flow from the hospital somehow limited, Is primary care, secondary care, nursing homes, hospital based, care patient hotels ,other service forms not effective enough. Is there a lack of cooperation between all these levels of healthcare?</p><p>*We have only one hospital so of 511 DRG groups we lost 317 and used only 194 groups, to solve this we would have to use 3 years or moreWe did a number of tests but non of them indicate that this was a problemIt would have been interesting to look at marital status to see if social status would affect their costLike nursing homes and other hospitals. This would have been very useful to locate with more accuracy the bottleneck in the flow from or within the hospital.</p><p>The purpose is not to decrease outliers we cant do thatThe proportion of outliers will always be the same but they will be relatively cheaperThe purpose is to influence the factors that cause the patient to be a outlier.According to this study, outliers are 7 times more expensive than average patient in the same DRG group and they cover between 10-12% of total hospital cost.</p><p>*It would be usefull to look at marital status and discharge modeLook at Connection between number of registered diagnosis and outliers within DRG groupAdd other relevant variables to the model such as Acuity, re-admission, waiting list, chronic diseases, test results.Limit the sample to smaller groups such as single DRG groups or MDC groups or speciality.Effect of quality of coding and homogeneity of DRG groups.connection beetv.</p><p>*Are female patients healthier than male patients. Are male patients sicker then female patients when they are admitted to hospital. Do male patients have less social-support system. What if all birth where excluded?</p><p>5. Increased need for nursing workload causes patient to be more likely to become an outlier. If primary and secondary care and hospital based care would be more effective could lower the number of patients that would eventually become an outlier. 6)</p><p>7) Increased number of medical diagnosis per patient increased the probability that a patient will become an outlier. Understandable, under coding therapeutically procedures known problem in LSH.</p><p>8) Incr...</p></li></ul>