Funding on Flat Per Diem Rate

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29th Nov 2017 Health Reference this

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1. According to the case scenario this report will discuss funding on flat Per diem rate in comparison with funding on DRG basis and there positive and negative aspects.

Flat per diem rate funding is defined as a prospective payment method in which a provider is reimbursed at a definite rate depending on the number of days a covered patient is hospitalised. To decide the cost by Per Diem method, number of days a patient is hospitalised are multiplied by per diem rate. In case previous data is not available then the providers and third party payers consider factors including volume of services provided, length of stay and how severe was the patient illness.

The advantages of per diem are due to the fact that as payment is made on daily basis the hospital benefits by increasing the length of stay and also enhance the number of inpatient admission. (2) Due to the hospital incentive involved more attention is given to patients and when after treatment of primary diagnosis is complete they shift to secondary diagnosis treatment.

The disadvantages of per diem are that as payments are made daily this method is not cost effective for the patients. (2) There is an increase in number of admissions and also the length of stay of patients.

The Diagnosis Related Group (DRG) system is a classification in which patients are grouped on basis of comparable diagnosis, treatment, utilisation of resources, cost and length of stay.(WHO 2007)

The merits are due to reason as DRG providers are recompensed on a fixed rate there is a cogent motivation for cost containment. (2) It is beneficial as the Length of stay and hospitalisation is reduced. (3) Earlier they were used for inpatients but now they are also used for ambulatory patients.(CISS 2005)

The demerits of DRG are the patients which require services for long term but are discharged early. (2) Though this system is speedy but in some cases the patient recovery is not up to the mark as a result there is an increase in number of readmissions.(Casto, Layman & Association 2006)

2. This report provides instances of DRG split and identifies the most recent version of AR-DRG used in Australia.

The instances where DRG had been split according to age and complexity involve cases of viral illness, bronchitis and asthma for the former and cases of diabetes, injuries for the later.

  1. T63A Viral illness Age>59 or W CC T63B Viral illness Age<60 or W/O CC
  2. E69A Bronchitis and Asthma Age>49 W CC

E69B Bronchitis and Asthma (Age<50 W CC) or (Age>49 W/O CC)

E69C Bronchitis and Asthma Age<50 W CC

  1. K60A Diabetes W Catastrophic or severe CC

K60B Diabetes W/O Catastrophic or severe C

  1. X60A Injuries Age>64 W CC

X60B Injuries Age>64 W/O CC (Ministry of Health 2009/10)

Splitting of AR-DRG in recent version of Victoria was in

AR-DRG D06Z Mastoid, sinus and complex middle ear procedures which was split into

Vic-DRG D06A Mastoid procedures

Vic-DRG D06B other sinus and complex middle ear procedure.(Department of Health 2014)

Most recent version of DRG being used in Australia is version 6.x for grouping the patients and establishing cost. The coding for diagnosis and the procedures utilised ICD-10-AM Eighth edition. AR-DRG version 6.x is the prescribed grouping for admitted patients of acute care for ABF (Activity Based Funding) in 2012-13 and 2013-14. Release of version 7.0 was due in July 2013 but will be enforced for ABF till July 2014.

AR DRG v6.0x reinstituted ten DRG’s from AR-DRG v5.0.The DRG’s which were added were malignancies of breast, mental health and maternity.

MDC 09 There was a split for malignant and non malignant diseases

MDC 14 Supplementary Patient Clinical Complexity Level split (PCCL)

MDC 19 A split for PCCL and age, split for mental health legal status

MDC 20 PCCL split for alcohol intoxication and withdrawl.

Patient Clinical Complexity Level- Is an evaluation of accumulative effect of patient comorbidities, complications and for each episode they are calculated.

Complication and Comorbidity level- It is the seriousness of diagnosis and values vary for medical and surgical episodes.(IHPA 2014a)

3. This report highlights the issues associated with coding practises involving rate of occurrence, causes, ramifications and solutions.

The problems associated with coding practises are known as DRG upcoding and are coding errors which occur when a patient event shifts to a DRG which has a higher restitution. In case of public hospitals it may be due to misconception between the doctor and the coder whereas in a private hospital it may be intentional.

The causes of DRG upcoding are due to Careless attitude when a coder is more concerned regarding productivity and standards are not given preference as a coder goes through huge medical records without paying much attention. (2) Sometimes after using codes repeatedly a memorised code without checking is entered and this is the reason for an error. (3) Inappropriate documentation may lead to error. (4) Encoder pathways are used by clinical encoders in the process of coding to determine DRG allocation and code. When an erroneous coding pathway occurs it results in allotment of an incorrect code. (5) False selection of primary diagnosis due to scarce knowledge of coding terminology and principles. (6) Missed secondary diagnosis

Due to DRG upcoding errors which occur because of misinterpretation between the doctor and the coder massive losses are caused to government.(Luo & Gallagher 2010)

During an investigation of Leukaemia and Lymphoma AR-DRG’s at a Sydney teaching hospital 242 episodes disclosed a level of miscoding which was considered mainly due to undercoding of comorbidities and complexities created an error rate of 15% in the DRG.(Reid, Allen & McIntosh 2005)

The solutions involve Inspection of original medical records. (2) Previous data should be compared to observe percent of elevated cost of DRG. (3) The most dependable method is code audit in which a knowledgeable health manager codes the original chart once again thereby comparing the codes which were deposited by the hospital with new codes. (4) It is a resource intensive procedure therefore it is conducted after a long time and very less patients are scrutinized.(Luo & Gallagher 2010)

4. This report will discuss the issues regarding use of average as a measure of central tendency and approaches in reference to AR-DRG regarding long stay of patients.

The value of mean in a dataset can be defined as sum of values of each observation which is divided by the number of observations.(Statistics 2013) Mean value as the characteristic value can be deceptive as it may mostly rely on extreme values

For instance if there were five patients in a ward with fractured leg

40+20+21+23+25/5 = 26

When all the observations are weighed equally the forty year old patient will cause an increase in the measure of central tendency and is not representative of the data which is available.

When data is available with less number of variables with uncommonly small or large numbers then in such cases median is used as a measure of central tendency. During calculation of median the values in a group of numbers are classified from highest to lowest.

Median is mostly used for demographic data with outliers or extreme values. When there are odd number of observations then the middle number is taken as median whereas in case even number of observation where an average of middle two values gives median.(Henderson 2009)

In case of a long stay outlier a basic amount of mean inlier cost is assigned to each event. For every outlier day a per diem is calculated by two ways –

1. In AR-DRG’s in which the duration of stay was methodical and wide to allow regression analysis the length of stay regression coefficient was per diem and it did not included the same day episodes.

2.For the remaining AR-DRG’s cost were divided into variable and fixed and a mean variable cost related to per day of patient was the basis for per diem cost.(IHPA 2014b)

5. This report will discuss about the about the comparison of peer hospitals. It also highlights the hospital areas and type of patients where benchmarks were not followed and the average Australia wide DRG.

For genuine comparison of hospitals within Australia the jurisdiction explores and executes approaches for the same. The jurisdiction accomplishes approaches which can assist the range and quality of data. A fair comparison among hospitals are carried out by peer groups independent of socio economic status of patients, size of hospital and the facilities provided. Hospitals are compared on the basis of efficiency, the healthcare staff including the number of doctors and number of beds in hospital. Case Mix Index (CMI) and category of hospital whether it is generalised or specialised are also considered.

There were certain cases where the areas of hospital could not maintain standards and led to a hospital acquired infection in patients which are also known as Nosocomial infections. The most common infections were caused due to bacteria because of a lack of proper hygiene methods. In Australia approximately 200,000 cases (Cruickshanck and Ferguson 2008) of hospital acquired infections were reported and it became the commonest complexity affecting patients in hospitals.(welfare 2012-13)

The ABF models are not able to reimburse hospital for the treatment of patient with major trauma in terms of cost. In case of trauma patients establishment of a DRG can be complicated due to the multiple injuries patient has suffered. Studies were conducted in many trauma centres worldwide. After research it was found that incidents of trauma had a cost of $178.7 million in 2008-09 in the state. The true cost for the trauma centre was $134 per day. Among the causes road trauma and violence were the major reasons. The increased cost was associated with injured body parts, length of stay, brain injury and whether the patient was admitted to intensive care unit.(Association 2014)

References

Association, A.H.a.H. 2014, ‘Activity based funding models can disadvantage trauma centres’.

Casto, A.B., Layman, E. & Association, A.H.I.M. 2006, Principles of healthcare reimbursement, American Health Information Management Association Chicago.

CISS 2005, ‘The Diagnosis Related Groups (DRG’s) to adjust payment mechanism for health system provider’.

Department of Health, V., Australia 2014, ‘Casemix funding history’.

Henderson, J. 2009, ‘Health Economics and Policy’.

IHPA, I.H.P.A. 2014a, ‘Admitted acute care’.

IHPA, I.H.P.A. 2014b, ‘DRG Inlier/Outlier model’.

Luo, W. & Gallagher, M. 2010, ‘Unsupervised DRG upcoding detection in healthcare databases’, Data Mining Workshops (ICDMW), 2010 IEEE International Conference on, IEEE, pp. 600-5.

Ministry of Health, N. 2009/10, ‘Costs of Care Standards’.

Reid, B., Allen, C. & McIntosh, J. 2005, ‘Investigation of leukaemia and lymphoma AR-DRGs at a Sydney teaching hospital’, Health Information Management Journal, vol. 34, no. 2, pp. 54-9.

Statistics, A.B.O. 2013, ‘Measure Of Central Tendency’.

welfare, A.I.o.h.a. 2012-13, ‘Australian Hospital Statistics 2012-13’.

WHO 2007, ‘Technical briefs for policy makers’, vol. Number 2.

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