Transcript Clinical Indicators of Diagnoses

Clinical Indicators of Diagnoses
The road to establishing a Clinical Indicator Team
Clinical indicators of diagnoses
What are they?
Facility approved clinical indicators that will establish the definition of
diagnoses that are
 Highly targeted for review by insurance companies
 Frequently inconsistent, incomplete, conflicting, missing or weak in
their documentation
 Conditions that are often queried by CDI for further specificity or
clinical relevance.
Clinical indicators of diagnoses
What are they?
Facility approved clinical indicators that will establish the
definition of diagnoses that are
 Highly targeted for review by insurance companies
 Frequently inconsistent, incomplete, conflicting, missing or
weak in their documentation
 Conditions that are often queried by CDI for further specificity
or clinical relevance.
Clinical indicators of diagnoses
Who benefits?
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CDI, coding, DRG appeals and denials management, PI and other
users of the clinical documentation
What are the benefits?
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Everyone will be on the same page.
Consistency though out the medical record
Reduce the number of CDI queries and coding retro queries
Reduce the potential of DRG denials
Provide additional backup to fight a DRG denial
Better quality outcomes
Clinical indicators of diagnoses
The “To do list”
 Establish the purpose and goals of the clinical indicators team
 Gather data to determine what diagnoses to start with
- data from DRG denials, RAC denials, and PI
- top CDI queries, mortality indicators
- enlist coding for CM and PCS coding roadblocks they are having
 Prioritize the top diagnoses to be targeted
 Get a head start on the evidence based clinical indicators of the top
diagnoses
 Start a list of others to invite to be on the team
- coding, PI, data abstractors, DRG and RAC denials team
Clinical indicators of diagnoses
The “To do list”
 Start a list of physicians that you would like to invite to participate
on the clinical indicator team
- find out who are the leads in their specialty service
 Have physician education plan outline
- Physician-to-Physician
 Need good organizational skills to keep track of meeting dates,
contacting committee members about the meeting and of their
responsibilities
Clinical indicators of diagnoses
What needs to be accomplished by the clinical indicator team
for each diagnosis?
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Definition
Evidence based clinical indicators
Documentation needed to support clinical validity
Education plan for physicians
Clinical indicators of diagnoses
Example:
Acute Respiratory Failure
Patients admitted to Brookhaven Memorial Hospital Medical Center
should meet the following clinical indicators, as established by the
Clinical Indicator Team, when documenting the diagnosis of Acute
Respiratory Failure.
Clinical indicators
Please document the above clinical indicators in the medical record.
If above indicators are not present, the findings to support the
diagnosis must be documented in the medical record.
Clinical indicators of diagnoses
Summary
 The Clinical Indicator Team should be focused on the quality
of the documentation.
 The need to support and validate the diagnoses documented
within the medical record is the priority, not reimbursement.
 Quality outcomes, hospital profiles, value-based purchasing,
and mortality rates will all improve when the documentation
is improved by clear, consistent, and complete
documentation.
Thank you
References
Wilk, D. (2016, May). Facilitywide Clinical Indictors for Quality,
Compliance and Reimbursement. Presented at ACDIS Conference,
Atlanta, Georgia
2016 ACDIS Recap
Drowning in a sea of data?
Strategically navigating data in CDI
Cynthia Hiddink RN, BSN, CCDS
7/22/2016
OBJECTIVES
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Learn how to use data to identify
opportunities and areas of focus.
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Better Understand utilization of analytical
tools to turn data into actionable insights.
Where does your data come from?
• EMAR
• Written progress notes
• Quality department
• Provider actions
• CDI activities
• Coding
• The boss
Traditional use of data in CDI
• Query rate (overall and by CDI specialist/physician)
• Physician response rate
(overall and by CDI specialist/physician)
• Physician agreement rate
(overall and by CDI specialist/physician)
• CC/MCC capture rates
• MS-DRG shifts
• Case-mix index changes
Ask the question first
• Why is our LOS so long in this particular DRG?
• Pull DRG with LOS over 5 days during a set time frame.
• Analyze
• Note trends, day of week of admission, date and time of
discharge order, MD lack of specificity…
• All data is actionable if you can drill down to the issue at
hand.
Next level opportunities
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RW of DRG doesn’t correlate with LOS
HGB drop/EF/VS
LOS variances
Single CC/MCC
Evaluation of admission source
Resource allocation
Time in EMAR/allocation
Timing of admissions
Prioritization of CDI work list and flow
Denials prevention
Quality – PSI, mortality (actual vs expected)
Predictive analytics
Denials prevention
data driven opportunities
• Acute respiratory failure with short LOS or without ABG
• PNA with no CXR
• Sepsis with short LOS
• Malnutrition with short LOS
• Extensive OR unrelated to procedure
• AKI without CC or with short LOS
• CVA with infarction with MCC and short LOS
Would you rather?
Or this
See this :
Visualization of data makes it easier to convey
Body Copy here:
Using data with Physicians
• SOI/ROM
• LOS
•average
•geometric mean
•expected
• Readmission rates
• Observed over expected mortality ratio
Peer to peer comparison
Competition is the best motivator
Quotes from the experts
• “Physicians will be engaged if they understand how documentation and
coding impacts their personal profile,” Judy Schade, RN, MSN, CCM,
CCDS, CDI Specialist at Mayo Clinic Hospital in Phoenix.
• “Physicians are by nature competitive, and so they aim to be high
achievers. CDI programs can use this to their advantage.” Judy Schade,
RN, MSN, CCM, CCDS, CDI Specialist at Mayo Clinic Hospital in Phoenix.
• “Be transparent so physicians can see the benefits—both financial and
quality-related—of precise documentation,” Karen Newhouser, RN, BSN,
CCDS, CCS, CCM, CDIP, Director of Education at Med-Partners in Tampa.
• “Then, drill down into the data to identify individual metrics, comparing
physicians against one another within the facility and within a
particular specialty or service line,” Michelle McCormack, RN, BSN,
CCDS, CRCR, Director of CDI at Stanford (California) Health Care.
Celebrate small victories
Make your goal in small achievable steps
Keep moving the mark forward
Conclusion
• Data becomes information that turns into action focused
education.
• Success is monitored to prove better documentation and
quality.
2016 ACDIS conference
Boosting buy-in: Using data to drive physician engagement
June 30, 2016–
CDI Journal - Volume 10, Issue 3
HCCs: Hierarchical
Condition Categories
BERNADETTE SLOVENSKY RN MSN CCDS
STONY BROOK MEDICINE
JULY 22, 2016
Risk Adjustment
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Risk adjustment is a corrective tool used by
actuaries to level the playing field regarding the
reporting of patient outcomes, adjusting for the
differences in risk among specific patients
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It allows for the adjustment of expected volumes to
account for the case mix of the facility or the
category being compared
Hierarchical Condition Categories
(HCC)
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Risk Adjusted Predictors of healthcare costs
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A Hierarchical Condition Category is a grouping of similar condition categories (CCs)
based on disease. Only the most severe manifestation of the disease is coded.
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Condition Category (CC) is a grouping of similar diagnosis codes into diseases that are
related clinically and with respect to cost.
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Initially used to set rates for Medicare Advantage (MA) Plans
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Now used to:
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Set rates for small group markets for ACA exchange plans
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Risk adjustment for many VBP measures
CMS Hierarchical Condition
Categories (HCCs)
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Each HCC has a score intended to predict the resources required to
treat a patient for one year via a risk coefficient score, which when
multiplied by a payment factor results in a payment amount to the
MA organization.
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The payment factor is unique to each MA organization, based in part
on its bid.
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The HCC payment is based on the organization’s bid amount and the
MA’s beneficiary's actual risk score.
CMS Hierarchical Condition
Categories (HCCs)
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Diagnosis codes within each HCC are related both
clinically and in cost to the fee-for-service Medicare
program.
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A patient may have multiple HCCs.
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In the CMS-HCC model some conditions have more than
one HCC, which differ by severity of the condition
(diabetes and cancer)
CMS Hierarchical Condition
Categories (HCCs)
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There are two HCC models
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CMS-HCCs for patients enrolled in Medicare Advantage
plans
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HHS-HCCs were created for the Affordable Care Act (ACA)
exchanges. It builds upon the CMS-HCC model but is more
complex. It is also referred to as the commercial model.
This presentation will focus on the CMS-HCC model.
CMS Hierarchical Condition
Categories (HCCs)
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In the model, ICD codes map to clinically related hierarchical
condition groups that are broadly organized into body systems.
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ICD-10 diagnosis codes are assigned to one of 189 HCCs.
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Codes collected from the current year are used to predict risk for the
next year. Codes collected from:
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Inpatient hospital stays
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Hospital outpatient claims
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Physician office claims
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Clinically trained non-physicians, e.g., NPs, PAs, Therapists, Certified
Wound Care Practitioners, Podiatrists, Psychologists
What Type of Conditions Map to a
CMS HCC?
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High Cost Medical Conditions (Current Cancer, heart
disease, hip fracture)
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Acute, Chronic, status codes, etiology and
manifestation
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Highest weighted: HIV, sepsis, opportunistic infections &
cancer
Amputaion, COPD, diabetic neuropathy
Common Conditions, rare conditions, curable and non
curable diseases, congenital and acquired…BUT
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They must be monitored, evaluated, assessed or treated
(MEAT)
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Monitor
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MEAT for the Chronic
Condition
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Example:
Evaluate
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“CHF well controlled with Lasix and ACE
inhibitor. Will continue current
medications”
“Major Depression – recurrent episode.
Patient continues with feelings of
hopelessness and anhedonia despite
current medication regiment of Zoloft
50 mg daily, Will increase dose to 100
mg daily and monitor”
Signs, Symptoms, Disease
Progression, Disease
Regression
Review of test results,
medication effective,
response to treatment
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Assess/Address
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(stable, improving,
exacerbation, worsening,
poor)
Ordering tests, discussion,
review records, counseling
Treatment
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Referral, medication,
planned surgery, therapies,
other modalities
Hierarchical Condition Categories
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Every code assigned should be at the most specific level possible based on
documentation.
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HCCs include diagnoses that are not CCs or MCCs in the MS-DRG
grouping system. So, it is important to capture all diagnoses with the
greatest specificity possible.
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In the inpatient setting, we tend to focus on capturing CCs and MCCs;
we need to code all conditions present with the greatest specificity
possible.
Where does the data Come
from?
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Most of the data that feeds into HCCs comes from Outpatient
Encounters
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Not All of HCCs are CC/ MCC
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HCC’s include 9548 ICD-10 Codes
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42% are CCs
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16% are MCCs
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That Leaves 42% that are neither CC or MCC
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HCCs That Are Not CCs/MCCs
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Some cancer diagnoses, e.g., Secondary Merkel cell
carcinoma, C7B.1
DM except that with ketoacidosis or hyperosmolarity or
other coma
Sickle cell without crisis
Many psychiatric diagnoses including alcohol and drug
dependence
AMI—except initial episode of care
‘X’ codes for suicide
Status codes including:
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Asymptomatic HIV status
Tracheostomy status
Gastrostomy status
Colostomy status
Amputation status
Long-term (current) use of insulin
Coding HCCs
Only E11.00 and
E11.01 are MCCs
E11.00
E11.01
E11.21
Physician documentation and coding
are critical for facilities operating
under a risk-adjustment system:
Medicare Advantage
Hospital inpatient VBP
Accountable Care Organizations
(ACOs)
Not all CCs and MCCs designated in
the MS-DRG grouping system are
included in the HCC code list..
None of these codes
is a CC/MCC
E11.22
Z89.611
Type 2 diabetes mellitus with hyperosmolarity
without nonketotic hyperglycemic-hyperosmolar
coma (NKHHC)
Type 2 diabetes mellitus with hyperosmolarity
with coma
Type 2 diabetes mellitus with diabetic
nephropathy
Type 2 diabetes mellitus with diabetic chronic
kidney disease
Acquired absence of right leg above knee
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18
18
189
Z89.612 Acquired absence of left leg above knee
Z89.619 Acquired absence of unspecified leg above knee
189
Z91.15 Patient's noncompliance with renal dialysis
Z93.0 Tracheostomy status
134
189
82
Z93.1
Gastrostomy status
188
Z93.2
Ileostomy status
188
Z93.3
Colostomy status
188
Other artificial openings of gastrointestinal tract
status
Z93.50 Unspecified cystostomy status
Z93.4
188
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Common Medicare Risk Adjustment
Coding/Documentation Errors
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Most specific ICD-10 code not assigned
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Discrepancies between diagnosis codes billed and diagnoses in the medical record
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Clinical documentation does not indicate if diagnoses are being monitored,
evaluated, or treated
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Status of patient’s cancer unclear, e.g., use of “history of”
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Chronic conditions not documented as “chronic”
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Diabetic complications not appropriately documented
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Record contains non-standard abbreviations or up and down arrows to indicate
diagnoses
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Unspecified and symptom diagnoses are not considered HCCs
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Questions????