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Introduction

Risk Adjustment is a statistical methodology used to predict and estimate the payment requirements to a health provider based on the patient’s health, their likely use of healthcare services and the cost of those services. It takes cognizance of the underlying status and health spending of an enrollee in an insurance plan when evaluating their health outcomes or health care cost (Juhnke, Bethge, & Muhlbacher, 2016). Risk adjustment equates the health status of a person to a number called the risk score or risk adjustment factor, which predicts healthcare costs and services. It is critical to ensuring adequate compensation to health insurance plans, so they maintain coverage and access to care for beneficiaries likely to incur higher than average costs (Reschke, & Sehlen, 2005).

Healthcare organizations and insurance companies to ensure they are accurately accounting for the health status of their patients and adjustment payments are made appropriately (CMS, 2008) use risk adjustment coding. The coding uses a set of diagnostic codes to indicate the health status of each patient that comes to their facilities. The code takes into account information such as patient’s age, sex and any pre – existing conditions or chronic illnesses they may have.

Risk adjustment coding is also important for health care providers, particularly those who work with high – risk patients. Without accurate risk adjustment coding, these providers might be unfairly penalized by companies, which could make it harder for them to stay in business or provide high – quality care (CMS, 2008).

Risk adjustment begins with a risk assessment, by which plan members are assigned risk scores. To perform a risk assessment, information about the enrollee that can be used to predict their costs is collected. That information includes the enrollee’s demographic characteristics and medical conditions. By measuring the relationship between these characteristics and costs for a large group of enrollees, a formula is developed that can be used to calculate a risk score for each individual based on what their costs are expected to be compared with others. The impact of each factor depends on its coefficient or weight in the formula. For example, the additional amount that it costs on average to provide care for an older patient, or a patient with hypertension, is measured and added to the risk scores of older individuals with hypertension. In risk adjustment, individuals’ risk scores are used to adjust the payments the plan receives to insure them (Schone & Brown, 2013)

The Risk Adjustment Factor (RAF) score also known as the risk score, an amalgamation of the demographic and diagnosis (disease risk scores) scores is the numeric value an enrollee in a risk adjustment programme is assigned each calendar year based on demographics and diagnosis (U.S Department of Health & Human Services, 2020). While demographic risk score is based on the patient age, community or medical institution, the diagnostic score is based on their present health conditions.

The Hierarchical Condition Categories (HCCs) are sets of medical codes linked to specific clinical diagnosis. The HCC list includes only the diagnosis that are likely to impact long – term healthcare costs related to clinical/and /or prescription drug management particular to the demographics of the special risk adjustment payment model.

HCC models are designed to predict the health spending for a specific patient population. In these models, the risk is equal to the level of expected healthcare spending. The HCC models use patient data to predict the estimated future costs for individual patients. The CMS-HCC model is prospective, meaning data is collected in the base year to determine expected costs for the following year (the “prediction” year). For example, data from 2023 (base year) will be used to predict expenses in 2024 (prediction year).

The HCC model was developed by examining how demographic characteristics and health diagnoses relate to health expenditures for the population under study. HCC models use two primary sources of data to determine a patient’s RAF: demographic characteristic and health status. Demographic data includes the patient’s age, gender, and other factors specific to the population. The second primary data source—health status—is based on ICD-10-CM diagnosis codes. While demographic data is straightforward, the collection and validation of patient diagnoses is complex. To identify the conditions that predict future healthcare costs, HCC models first organize diseases and conditions into body systems or disease processes, called diagnostic groups. Conditions in each diagnostic group are further organized into condition categories. ICD-10-CM diagnosis codes are ranked into categories that represent conditions with similar cost patterns (CMS, 2008).

Since 2004, the Centers for Medicare and Medicaid Services (CMS) has used the HCC as part of a risk adjustment model that identifies individuals with serious acute or chronic conditions. The CMS uses the CMS – HCC risk adjustment model for the Medicare Advantage program and those who qualify for Medicare or patients 65 years and above, calculating risk payments for the next year for individuals and small group markets under the Affordable Care Act (CMS, 2011). The main goal of the CMS – HCC risk adjustment model is to estimate the cost to treat a patient in a given year, based on the patient’s specific health condition.

The CMS – HCC risk score for a beneficiary is the sum of the score or weight attributed to each of the demographic factors and HCC within the model. The CMS – HCC model is normalized to 1.0. Beneficiaries would be considered relatively healthy, and therefore less costly, with a risk score less than 1.0 (Kautter et al, 2014). Thus, The RAF for the average patient is 1.0. Healthy patients have a below-average RAF (less than 1.0) while sicker patients have an above-average RAF (greater than 1.0).

Question No.2

Patient Number One – Physician Office Visit

Purpose of Health Inquiry

The patient is a 66 – year – old female with conditions of inflammatory polyneuropathy and cardiomyopathy. She visited her family physician for annual physical and follow – up examination.

Description of Diagnosis

Inflammatory neuropathies are acquired disorders of peripheral nerves and occasionally of the central nervous system that can affect individuals at any age. They are classified as either acute or chronic. The acute form reaches maturity by 4 weeks and the chronic form at over 8 weeks or more. The most common chronic inflammatory neuropathy is Chronic Inflammatory demyelinating Polyneuropathy (CIPD). Chronic Inflammatory demyelinating Polyneuropathy (CIPD) is a neurological disorder caused by an abnormal immune response and damage to the fat – based protective covering on nerves called the myelin sheath. It happens when the immune system attacks the myelin cover of the nerves. Considered an autoimmune disease, healthcare providers regard CIPD as a chronic form of Guillain – Barre’s Syndrome (in which the immune system mistakenly attacks the body) and considered the long-term part of the disease.

The symptoms of CIPD include;

  1. Tingling or no feeling in fingers and toes.
  2. Weakness of arms and legs
  3. Loss of deep tendon (muscle stretch) reflexes.
  4. Fatigue or feeling tired
  5. Unusual feelings in the body.

It is most common in young adult men, but can happen at any age and in both genders.

On the other hand, Cardiomyopathy, the medical term for a weak heart, is an acquired or hereditary disease that affects the heart muscle, making the heart inefficient and unable to adequately pump blood to the rest of the body, and can lead to heart failure. The part of the heart damaged by cardiomyopathy is the heart main pumping chamber (left ventricle). The effects of cardiomyopathy is decreased heart functions, which expresses itself in fatigue, shortness of breath or heart palpitations. The decreased heart functions also affects the lungs, liver and other body functions and could get worst over time (Lusher, 2016)

There are three types of cardiomyopathy – Diated Cardiomyopathy (DCM), Hypertropic Cardiomyopathy (HCM) and Restrictive Cardiomyopathy (RCM). DCM is the most common form of cardiomyopathy in children, in this condition the heart becomes enlarged and does not work well, as a result the heart cannot pump enough blood out to the body. HCM is the condition in which the heart muscle becomes thick. This makes it hard for blood to leave the heart; this type of cardiomyopathy is most often passed down through families.

RCM is a group of disorders. The heart chambers are unable to fill with blood because the heart muscle is stiff. The most common cause of this cardiomyopathy are amyloidosis and scarring of the heart from an unknown cause.

Estimation of Risk Adjustment Score for Patient Number 1

Table 1: Diagnosis Table for Patient Number 1

Sequence

CD – 10 Code

Code Description

HCC

Risk Score

PDX

Z01.419

Encounter for gynecological examination without abnormal finding

0

 

SDX

G619

Inflammatory polyneuropathy unspecified

75

0.491

SDX

I429

Cardiomyopathy unspecified

85

0.31

PDX

Z23

Encounter Immunization

0

 
   

Total Risk Score (Diagnosis)

 

0.801

   

Risk Score (Demographic)

 

0.306

   

Total raw Risk Score

 

1.107

   

2023 Normalization factor

 

1.050

   

Risk Adjustment Score

 

1.054

Code Z01.419 - Encounter for gynecological examination without abnormal finding; is listed by World Health Organization (WHO) under the range – factors influencing health status and contact with health services. It is a billable/ specific ICD – 10 – CM code that can be used to indicate a diagnosis for reimbursement purposes and applicable to female patients. It is exempt from POA reporting. Code Z23 – Encounter Immunization is a billable/specific ICD – 10 – CM code that can be used to indicate a diagnosis for reimbursement purposes and are captured under the factors influencing health status and contact with health services. It involves administration of vaccines during clinical visits (Chernew, Carichner, Impreso et al., 2011). The risk score for an average Medicare beneficiary is 1.0, the patient risk adjustment score is 1.054. The patient risk score is slightly higher than the average Medicare beneficiary.

Patient Number Two – Physician Office Visit

Purpose of Health Inquiry

The patient is a 68 years old male suffering from morbid obesity. His conditioned worsened due to inability to limit food to 7,000 calories/day. He continues to eat excessively. During the hospital visit, he was examined for exacerbation of Congestive Heart Failure (CHF); chronic obstructive Pulmonary Disease (Emphysema) and chronic ulcer of the thigh due to atherosclerosis.

Description of Diagnosis

Obesity is a disorder involving excessive fat that increases the risk of health problems. It results from taking in more calories than are burned by exercises and normal activity (Bouchard, 2010). Obesity and its various stages are measured with the use of Body Mass Index (BMI).

BMI = Weight (Kg)/Height (m2)

BMI is a screening tool that measures the ratio of height to weight and in most people, BMI relates to body fat. Several factors contribute to developing obesity, including genetic factors; hormone imbalances; socioeconomic and geographical factors; cultural factors and environmental factors (Choquet, & Meyre, 2011).

BMI ranges (in kg/m2) are listed as follows:

  1. Underweight : BMI less than 18.5
  2. Optimum range : BMI 18.5 – 24.9
  3. Overweight : BMI 25 – 29.9
  4. Class I Obesity: BMI 30 – 34.9
  5. Class II Obesity : BMI 35 – 39.9
  6. Class III Obesity : BMI more than 40

Morbid obesity is class III obesity, and sometimes referred to as clinically severe obesity. It is a health condition resulting from an abnormally high body mass that is diagonized as BMI greater than 40kg /m2 , a BMI of greater than 35kg /m2 with at least one serious obesity – related condition or weight which is more than 80 to 100 pounds (37kg to 45kg) above the ideal body weight. The exact cause of morbid obesity are not understood but there are likely many factors involved. In Morbidly obese individuals the major issue is that, the stored body energy is too high. This may be due to low metabolism with low energy expenditure, excessive calorific intakes or a combination of both.

Morbid obesity can also be measured by an unhealthy body fat distribution that healthcare providers estimate by measuring the waist circumference and skin thickness. Other forms of estimating obesity is by measuring skin thickness in the following areas of the body;

  • The back of the upper arms (triceps)
  • The front of the upper arms (biceps)
  • Under the shoulder blades (subscapular)
  • Above the hipbones (supra - iliac).

Heart failure (sometimes called Congestive heart failure CHF) is a serious long-term condition resulting from the inability of the heart to pump blood well enough to provide a normal body supply. Heat failure does not imply that the heart has literally failed or is about to stop working, rather it refers to the situation in which the heart muscles have become less able to contract over time or has a mechanical problem that limits the ability to fill with blood. This results in the heart muscle not being able to keep up with the body’s demand and blood returns to the heart faster than it can be pumped out. This leads to congestion or backed up conditions. This pumping problem means that not enough oxygen – rich blood can get to the body’s other organs.

The impact of this situation is that the body tries to compensate in different ways. The chamber of the heart may respond by stretching to hold more blood to pump through the body or by becoming stiff and thickened. The heart beats faster to take less time for refilling after it contracts – but over the long run, less blood circulates, and the extra effort can cause heart palpitations. The heart also enlarges to make room for the blood and the lungs fill with fluid, causing shortness of breath, and the heart muscle walls may eventually weaken and become unable to pump as efficiently.

The kidneys, not receiving enough blood, may respond by causing the body to retain water and sodium, which can lead to kidney failure. If fluid buildup in the arms, legs, ankles, feet, lungs, or other organs, the body becomes congested. Congestive heart failure is the term used to describe the condition and requires urgent and timely medical attention. With or without treatment, heart failure is often and typically progressive, meaning it gradually gets worse. Some of the major causes of Congestive heart failure (CHF) is coronary artery disease, heart attacks, diabetes, high blood pressure, cardiomyopathy or valvar heart disease. The risk factors for coronary artery diseases include high levels of cholesterol and /or triglyceride in the blood. Early warning signs of CHF include;

  1. Shortness of breath with activity or when lying down
  2. Fatigue and weakness
  • Swellings in the legs, ankles and feet
  1. Rapid or irregular heartbeat.
  2. Reduced ability to exercise.
  3. Wheezing
  • A cough that does not go away or a cough that brings up white or pink mucus with spots of blood
  • Swelling of the belly area.

Emphysema is a Chronic Obstructive Pulmonary Disease (COPD) and preventable respiratory lung disease. It develops over time and involves the gradual damage of lung tissue, specifically the destruction of the alveoli (tiny air sacs). Gradually, this damage causes the air sacs to rupture and create one big air pocket instead of many small ones. Sacs are normally elastic or stretchy, allowing each air sac to fill up with air, like small balloons during the breathing process (Antioch, & Walsh, 2002). In emphysema, the walls between many of the air sacs in the lungs are damaged. These causes the air sacs to lose their shape and become floppy. The damage also can destroy the walls of the air sacs, leading to fewer and larger air sacs instead of many tiny ones. This makes it harder for your lungs to move oxygen in and carbon dioxide out of your body.

The damage of the air sacs also leads to reduction in the lung surface area, this traps air in the damaged tissue and prevents oxygen from moving through the bloodstream. Additionally, the blockage causes the lungs to slowly overfill and makes breathing increasingly more difficult. The main causes of emphysema is smoking. Other causes include air pollution and chemical fumes; and other long – term exposure to irritants that damage the lungs and airways, such as secondhand smoking and dusts from environment or workplace. Signs and symptoms of emphysema take years to develop and includes shortness of breath, coughing with mucus, wheezing, chest tightness and fatigue.

Estimation of Risk Adjustment Score for Patient Number 2

Table 2: Diagnosis Table for Patient Number 2

Sequence

CD – 10 Code

Code Description

HCC

Risk Score

PDX

E6601

Morbid obesity due to excess of calories

22

0.262

SDX

I509

CHF unspecified

85

0.31

SDX

J439

Emphysema, unspecified

111

0.335

PDX

I70.231

Atherosclerosis of native arteries of right leg with ulceration of thigh

106

1.537

   

Total Risk Score (Diagnosis)

 

2.334

   

Risk Score (Demographic)

 

0.306

   

Total raw Risk Score

 

2.640

   

2023 Normalization factor

 

1.050

   

Risk Adjustment Score

 

2.514

The risk score for an average Medicare beneficiary is 1.0, the patient risk adjustment score is 2.514. The patient risk score is higher than the average Medicare beneficiary.

Patient Number 3 – Hospital Inpatient Admission

Purpose of Health Inquiry

The patient is an unspecified female with anemia and condition of carcinoma of the pancreatic head with prevailing Hgb of 9.1 at the time of admission. She received Docetaxel chemotherapy. At the time of leaving the hospital, the patient comorbidities on discharge after attaining Hgb 11.3 include ventricular tachycardia.

Description of Diagnosis of Patient 3

The pancreas is an organ that sits behind the stomach. It is shaped a bit like a fish with a wide head, a tapering body, and a narrow, pointed tail. In adults it is about 6 inches (15 centimeters) long but less than 2 inches (5 centimeters) wide. The head of the pancreas is on the right side of the abdomen (belly), behind where the stomach meets the duodenum (the first part of the small intestine) (The European Study group, 2018). The body of the pancreas is behind the stomach and the tail of the pancreas is on the left side of the abdomen next to the spleen. The pancreas secrets enzymes that aid digestion and hormones that help regulate the metabolism of sugars

Pancreatic cancer is a type of cancer that starts in the pancreas. Cancer starts when cells in the body begin to grow out of control. Carcinoma is the most common form of cancer. It starts in the epithelial tissue of the skin or internal organs. Carcinoma of the pancreatic head is the most common type of pancreatic cancer, accounting for more than 90% of pancreatic cancer diagnosis. This cancer occurs in the lining of the ducts in the pancreas. This type of cancer is often detected late, spreading rapidly and has a poor prognosis. The initial manifestations of this type of pancreatic cancer are often nonspecific, and consequently are often misinterpreted. In the later stages, they are associated with symptoms but these can be non – specific, such as lack of appetite and weight loss.

Other specific symptoms of carcinoma head of pancreas are;

  • Abdominal pain that radiates to the back
  • Yellowing of the skin and whites of the eyes (jaundice)
  • Light – coloured stools
  • Dark coloured urine
  • Itchy skin
  • New diagnosis of diabetes or existing diabetes that is becoming more difficult to control
  • Blood clots

Hgb expresses the amount of hemoglobin in the whole blood and expressed in grams per deciliter (g/dl). The normal hgb level for males is 14 to 18 g/dl, which for females is 12 to 16 g/dl. When the hemoglobin level is low, the patient has anemia. When hemoglobin level is low, it means the body is not getting enough oxygen, making the person tired and weak.

Comorbidity is the simultaneous presence of two or more diseases or medical conditions in a patient (Sorace, Wong, Worrall, Kelman, Saneinejad, & MaCurdy, 2011). Conditions described as comorbidities are often chronic or long – term conditions. Tachycardia is a very fast heart rate of more than 100 beats per minutes. The many forms of tachycardia depends on where the fast heart rates begins. If it begins in the ventricles, it is called ventricular tachycardia. If it begins above the ventricles, it is called supraventricular tachycardia.

Ventricular tachycardia (VT) is a heart rhythm problem (arrhythmia) caused by irregular electrical signals in the lower chambers of the heart. It is defined as three or more heartbeats in a row; at a rate, more than 100 beats a minute. If VT lasts for more than a few seconds at a time it can become life – threatening.

Most people with VT have other heart problems that caused it, such as;

  • Coronary artery disease
  • Heart failure
  • Myocarditis
  • Enlarged heart (cardiomyopathy)
  • Heart valve disease
  • Previous heart attack (myocardial infarction) or heart surgery that made scar tissue on the heart.

Estimation of Risk Adjustment Score for Patient Number 3

Table 3: Diagnosis Table for Patient Number 3

Sequence

CD – 10 Code

Code Description

HCC

Risk Score

PDX

D6481

Anemia due to antineoplastic chemotherapy

0

0

SDX

C250

Malignant neoplasm of head of pancreas

9

1.027

SDX

I472

Ventricular tachycardia

96

0.271

   

Total Risk Score (Diagnosis)

 

1.298

   

Risk Score (Demographic)

 

0.306

   

Total raw Risk Score

 

1.604

   

2023 Normalization factor

 

1.050

   

Risk Adjustment Score

 

1.527

D6481 – anemia due to antineoplastic is a medical classification listed by WHO under the range – disease of the blood and blood – forming organs and certain disorders involving the immune mechanism. It is billable.

The risk score for an average Medicare beneficiary is 1.0, the patient risk adjustment score is 1.527. The patient risk score is higher than the average Medicare beneficiary.

Patient Number 4 – Hospital Inpatient Admission

Purpose of Health Inquiry

The patient presumably male has knee pain, with BKA infection at the stump end. The patient had other medical conditions treated and monitored during admission in the hospital including: Parkinson’s disease; hypertensive heart disease; congestive heart failure; bilateral capsular glaucoma, old MI six months ago, and recent abnormal cardiac stress test. Based on evaluation, the left knee stump indicated patient has cellulitis at the stump end and was treated with antibiotic regimen.

Description of Diagnosis of Patient 4

A below-knee amputation (BKA), or below-the-knee amputation, is a transtibial amputation that involves removing the foot, ankle joint, distal tibia, fibula, and corresponding soft tissue structures. Lower extremity amputation serves as a life-saving procedure. Lower limb ischemia, peripheral arterial disease, and diabetes are considered the major causality of limb amputations in more than 50% of cases (Petersen, 1968). Generally, a BKA is preferred over an above-knee amputation (AKA), as the former has better rehabilitation and functional outcomes. The leading cause of BKA is complications from diabetes, such as peripheral vascular disease, open wounds, and infection. Prevention and management of diabetes and lower-extremity circulation problems can greatly reduce the risk of developing conditions that lead to the need for lower-extremity amputation.

Cellulitis is a common bacterial skin infection that causes redness, swelling, and pain in the infected area of the skin. If untreated, it can spread and cause serious health problems. Good wound care and hygiene are important for preventing cellulitis. There are different types of cellulitis, depending on where the infection occurs. Some types include: periorbital cellulitis, which develops around the eyes. Facial cellulitis, which develops around the eyes, nose, and cheeks (NcNamara, Tleyjeh, Berbari, Lahr, & Martinez, 2007). Cellulitis is caused when bacteria, most commonly streptococcus and staphylococcus, enter through a crack or break in the skin.

The symptoms of Cellulitis include;

  • Fever with chills and sweating.
  •  
  • Pain or tenderness in the affected area.
  • Skin redness or inflammation that gets bigger as the infection spreads.
  • Skin sore or rash that starts suddenly, and grows quickly in the first 24 hours.
  • Tight, glossy, stretched appearance of the skin.

Estimation of Risk Adjustment Score for Patient Number 3

Table 4: Diagnosis Table for Patient Number 3

Sequence

CD – 10 Code

Code Description

HCC

Risk Score

PDX

T87.44

Extremity

189

0.567

SDX

LO3.116

Cellulitis of left lower limb

0

0

SDX

B95.61

MSSA

0

0

SDX

G20

Parkinsonism

78

0.686

SDX

I110

Hypertensive heart disease with heart failure

85

0.31

SDX

I50.9

CHF, Unspecified

85

0.31

SDX

H40.1431

Capsular glaucoma with pseudo exfoliation of lens, bilateral, mild stage

0

 

SDX

I25.2

Old myocardial infarction

0

 

SDX

R94.39

Abnormal result of other cardiovascular function study

0

 

SDX

Z89. 512

Acquired absence of left leg below knew

189

0.567

   

Total Risk Score (Diagnosis)

 

2.44

   

Risk Score (Demographic)

 

0.306

   

Total raw Risk Score

 

2.746

   

2023 Normalization factor

 

1.050

   

Risk Adjustment Score

 

2,615

The codes LO3.116; B95.61; H40.1431; I25.2 and R94.39 are billable (Kronick, & Welch, 2014).

The risk score for an average Medicare beneficiary is 1.0, the patient risk adjustment score is 2.615. The patient risk score is higher than the average Medicare beneficiary.

Question Number 3

Which condition were you surprised was not included in the CMS HCC risk adjustment model?

I was surprised that the following under listed conditions were not included in the CMS HCC risk adjustment model. This condition affects several old citizens and women, who are increasingly becoming vulnerable to the effects of age and climate change.

The conditions are:

  1. Gynecological examination without abnormal finding.
  2. Immunization
  3. Anemia due to antineoplastic chemotherapy
  4. Capsular glaucoma with pseudo exfoliation of lens, bilateral, mild stage.
  5. Old myocardial infarction (Centers for Medicare & Medicaid Services (CMS), HHS, 2016)

Which condition were you surprised was included in the CMS HCC risk adjustment model?

I was surprised that the following were included in the CMS HCC risk adjustment model because I do not consider them very chronic and important

The conditions are:

  1. Typhoid pneumonia
  2. Human Immunodeficiency Virus (HIV), Type 1 and 2
  3. Substance use disorders
  4. Schizophrenia

Which condition’s risk score was lower than expected?

The following conditions were lower than expected.

  1. Chronic Hepatitis
  2. Fibrosis of lung and other chronic lung disorder
  3. Other significant Endocrine and metabolic disorders
  4. Breast prostrate, and other cancers

Which condition’s risk score was higher than expected?

The following conditions were higher than expected

  1. Malignant neoplasm of head of pancreas
  2. Atherosclerosis of native arteries of right leg with ulceration of thigh
  3. Metastatic cancer and acute Leukemia

Explain of adjustment model to a family member new to Medicare Advantage, using non – technical terms.

In simple terms, risk adjustment models gives us a way to determine what to pay to a health provider (health insurance) based on a patient’s health, considering what type of health care services (hospitals) and the cost of those services (Hayford, & Burns, 2018).

Risk adjustment models seek to answer the following questions.

  1. What are the risk factors used in assessing the patients current health conditions
  2. Can the risk factors be combined
  3. Which are the currently available risk adjustments used by local and international healthcare systems.

References

Antioch, K., & Walsh, M. (2002). Risk-adjusted capitation funding models for chronic disease in Australia: alternatives to casemix funding. The European Journal of Health Economics. 2002; 3(2):83–93. Doi: 10.1007/s10198-002-0096-7. 

Bouchard, C. (2010) Defining the genetic architecture of the predisposition to obesity: a

Centers for Medicare & Medicaid Services (CMS), HHS. (2016). Medicare program; Medicare shared savings program; accountable care organizations—revised benchmark rebasing methodology, facilitating transition to performance-based risk, and administrative finality of financial calculations. Final rule. Fed Regist. 2016; 81(112):37949–38017.

Challenge but not insurmountable task. Am. J. Clin Nutr, 91: 5 – 6

Chernew, M.E., Carichner, J., Impreso J, et al. (2021). Coding-driven changes in measured risk in accountable care organizations. Health Aff. 2021; 40(12):1909–1917. Doi: 10.1377/hlthaff.2021.00361

Choquet H, & Meyre D. (2011) Genetics of Obesity. What have we learnt?

CMS.(2008) Report: Risk Adjustment Data Technical Assistance For Medicare Advantage

Organizations Participant Guide.

From: www.csscoperations.com/Internet/Cssc3.Nsf/files/participant-guide- publish_052909.pdf/$File/participant-guide-publish_052909.pdf.

CMS. (2011) report: evaluation of the CMS-HCC risk adjustment model. CMS. Available from: https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors-Items/Evaluation2011. Accessed September 28, 2022. [Google Scholar]

Curr Genomics 12: 169 – 79

Doi: 10.5334/ijic. 2500

Hayford, T.B., & Burns, A.L. (2018). Medicare advantage enrollment and beneficiary risk scores: difference-in-differences analyses show increases for all enrollees on account of market-wide changes. Inquiry. 2018; 55. Doi: 10.1177/0046958018788640 

In Integrated Healthcare systems. Int J Integr Care, Oct – Dec; 1694):4

Juhnke, C., Bethge, S., & Muhlbacher, A.C. (2016). A review of Risk adjustment and their use

Kautter, J,. Pope, G.C; Inger, M; Freeman, S; Paterson, L; Cohen, M and Kenan, P (2014)

The HHS – HCC Risk Adjustment model for individual and Small Group Market

Under the Affordable Act.

Medicare Medicaid Res Rev 4(3): mmrr2014 – 004- 03 – a03 

Doi: 10.560/mmrr.04.03.a03.

Kronick, R., & Welch, P. (2014). Measuring coding intensity in the Medicare advantage program. Medicare Medicaid Res Rev. 2014; 4(2). Doi: 10.5600/mmrr.004.02.sa06 .

Lusher, H (2016) Cardiomyopathies: definition, diagnosis, causes and genetics. European Heart Journal, Volume 37, Issue 23, Pages 1779 – 1782

https/doi.org/ 10.1093/eurheartj/ehw254.

NcNamara, D.R., Tleyjeh, E.F., Berbari, B.D., Lahr, J.W. & Martinez, S.A. (2007). Incidence of lower – extremity cellulitis: AS population based study. In Mayo Clin Proc 82. P. 817

Neoplasm, Gut, Volume 67, issue 5, 789 – 804

Petersen H.E. (1968). The problem of the geriatric amputte. Artif Limbs Autumn 12 (2).

Reschke, P., & Sehlen, S. (2005). Methoden der Morbiditätsadjustierung. Gesundheits-und Sozialpolitik. pp. 10–9. 

Schone, E & Brown R. (2013) Risk Adjustment: What is the Current State of the Art and how can it be Improved? Research Synthesis Report, No.25, July. Pinceton: Robert Wood Johnson Foundation;

Sorace, J., Wong, H.H., Worrall, C., Kelman, J., Saneinejad, S., & MaCurdy T. (2011). The complexity of disease combinations in the Medicare population. Popul Health Manag. 2011; 14(4):161–166. Doi: 10.1089/pop.2010.0044

The European Study group (2018) European evidence – based guidelines on pancreatic cyst

U.S Department of Health & Human Services. (October 20, 2020). Risk adjustment methodology an overview of risk adjustment. In: module 1: risk adjustment introduction and overview. Available from: https://www.hhs.gov/guidance/sites/default/files/hhs-guidance-documents/prep-act-guidance.pdf. Accessed March 23, 2023.

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