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A Little Look at Big Data Chris Stout, PhD, Vice President Research and Data Analytics

CSM 2017 Stout

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A Little Look at Big Data

Chris Stout, PhD, Vice President Research and Data Analytics

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Well…

Why is working in healthcare so hard…?

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It was nice to come to ATI work with workers’ comp outcomes because…

Outcomes are VERY Quantified– RTW at the same job description

and PDL or not?– How many days passed before

RTW?– Nice, clean, and tidy!

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I was always frustrated with the disconnect of collecting

PROs in real-time for the clinician (as well as me!)

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But we may have cracked the code

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Passionate about Patient Satisfaction: Since its inception, ATI has been focused on our mission to provide the highest quality of care in a friendly and encouraging environment. We have the most inclusive, methodologically sound, and productive program in physical therapy. Last year alone, we sent out 222,354 patient satisfaction surveys and received 55,082 in return (a 25% response rate).• Each day, returned surveys are scanned into our IT infrastructure and are immediately

available to the Clinic Director and Operations Leadership. This allows the Clinic Director to share praises with the staff, as well as address anything that is not exceeding expectations related to quality of care or customer service. It is a concrete example of how the benefit of a strong IT platform enables ATI to maintain an extremely high-touch management environment where clinicians and managers can be immediately responsive to patient feedback.

• We are not content with small samples or biased data, so ATI invested in industry-leading methodology and was published in Advance for Physical Therapy for “What Patients Want: Innovative uses of patient satisfaction data in quality improvement and clinical management.”

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ATI also introduced the use of the Net Promoter Score (NPS) to the physical therapy industry. The NPS is a customer loyalty metric used across many industries, including healthcare. It was introduced in Fred Reicheld’s 2003 Harvard Business Review article on the topic. Patients are asked, on a scale of 0-10, how likely they are to recommend ATI to friends and family. ATI outperforms many other well-known companies, which is a reflection of our commitment to delivering on our mission for every patient, every day.

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Pioneering Patient Outcome Management in PT: ATI embedded a complete set of functional outcome tools directly into our EHR that are concise, easy to complete, reliable, valid, and universally recognized and respected by professionals in the field. They are immediately scored, have descriptive pop-up result information, and provide patient item responses. The findings are available to the clinician in real-time, and are aggregated for post-discharge analyses.

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Leveraging quality clinical outcomes and member satisfaction scores, the Patient Outcomes Report establishes a baseline of the existing care continuum and its impact on patients’ quality of life. This customizable tool facilitates the creation and implementation of care plans that enhance clinical effectiveness, reduce the cost of care, and improve the patient experience.

MSKore is a proprietary tool developed by ATI to reference various descriptive analytical aspects of patient care specific to musculoskeletal (MSK) conditions

Enhancing Patient Clinical Outcomes While Favorably Influencing the Episodic Cost of Care for Musculoskeletal (MSK) Conditions

MSKore®

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• 41% of the population in this examination was male, 59% female.• Most were between the ages of 50 to 59, with females exceeding males in this

age group. • The majority of patients fall into the normal category, followed by those

considered to be overweight.

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Female Male

12,520

9,11642% of the population in this examination was male, 58% female.

Most were between the ages of 50 to 59, with females far exceeding males in this age group.

The majority of patients fall into the obese category, followed by those considered to be overweight.

3 %

32%

32%

33%

Patient Demographics

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Referral Diversity: Percentage of total referrals by physician specialty

Physician SpecialtyOrthopedic

Family Practice

Internal Medicine

PodiatristPhysician Assistant

Neurosurgeon

Physical Medicine and Rehabilitation

56%12%

7%

2%

3%9%2%

Physician Demographics

The Majority of referrals came from Orthopedic Physicians Distant second was Family Practice and Internal Medicine

Physicians

15,000

5,000

0

10,000

Orthopedic Family Practice Internal Medicine

Physician AssistantPodiatrist

Neurosurgeon

Pediatric

Physical Medicine & RehabilitationOB/GYN

Health Care Education Nurse Practitioner

Other* Neurologist

All Referring Physician: The number of referrals by type

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As XYZ-Comp may have regions in Illinois that would benefit from more outpatient treatment venues as well as improved rural outpatient coverage, this examination notes regions of Member density and potentials of partnership.Patient Distribution by

Clinic

ATI Investment in Market-Specific Outpatient Therapy

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Physical Therapy at ATI

Body Part Total Number of Patients

Mean PT Duration Days

Average Number of Comorbids

Most frequently occurring comorbidity

Neck 987 xxx 2.6 ArthritisShoulder 1919 Xxx 2.2 ArthritisElbow/Wrist/Hand 765 Xxx 2.2 ArthritisLow back/Lumbar spine 2265 xxx 2.8 Arthritis

Hip 879 Xxx 2.6 ArthritisKnee 2309 Xxx 2.2 ArthritisFoot 1429 Xxx 1.8 Other Allergy

Totals 10553 xx.x 2.3 Arthritis

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General Health Measures

Instrument SF-12

Body PartSubjective General Health

Status ADLs and Functionality Pain/ Discomfort Mood/ Emotion/ Affect

Mean Pre-tx Mean Post-tx Mean Pre-tx Mean Post-tx Mean Pre-tx Mean Post-tx Mean Pre-tx Mean Post-txNeck 2.9 2.3 5.4 3.7 3.9 2.5 6.5 4.6

Shoulder 2.4 1.9 5.3 3.3 3.4 2.1 5.0 3.6Elbow/ Wrist/Hand 2.2 1.8 5.4 3.5 3.1 2.1 4.9 3.7Low back/ Lumbar

spine 3.1 2.4 6.7 4.6 4.0 2.6 6.7 5.0

Hip 2.6 1.9 6.8 4.0 3.5 2.0 5.7 3.8Knee 2.6 2.0 6.9 4.0 3.6 2.1 5.7 3.9Foot 2.4 1.9 5.9 3.4 3.1 2.0 4.9 3.6

Total 2.6 2.0 6.1 3.8 3.5 2.2 5.6 4.0

NOTE: Lower scores indicate improvement. Red scores indicate lack of improvement.

SF-12 Subjective General Health Status ADLs and Functionality Pain/ Discomfort Mood/ Emotion/ Affect

Clinical Interpretation 0 = Excellent General Health Status 1 – 2 = Very Good General Health Status 3 – 4 = Good General Health Status 5 – 6 = Fair General Health Status 7 – 8 = Very Poor General Health Status

Clinical Interpretation

0 = No Limitations 1-2 = Good Functionality 3-4 = Few Limitations 5-7 = Minimal Limitations 8-9 = Moderate Limitations 10-11 = Marked Functional Limitations 12 = Profound Functional Limitations

Clinical Interpretation 0 = Pain Free 1 – 2 = Minimal Pain 3 – 4 = Moderate Pain 5 – 6 = Marked Pain 7 – 8 = Extreme Discomfort

Clinical Interpretation 0 = No Emotional Concerns 1 – 5 = Few Emotional Concerns 6 – 10 = Mild Emotional Concerns 11 – 15 = Moderate Emotional Concerns 16 – 20 = Extreme Emotional Concerns

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Payer

2016 Clinical Staff & Customer Service Clinic Facilities

RESP #Patient

Satisfaction

Clinical quality & treatment

Professional attitude, & appearance of all staff

Customer Service of all Staff

Billing and Payment process explanation

Were clearly defined goals set for your treatment?

Were your treatment goals achieved

Overall comfort & appeal of clinic

Location of clinic

XYZ 1696 92.94% 98.21% 99.45% 98.59% 89.87% 93.82% 95.44% 97.32% 99.37%ALL ATI 28877 93.68% 98.09% 99.10% 98.62% 93.12% 94.23% 94.50% 96.82% 99.30%

Quality and Patient Satisfaction

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ATI Patient Outcomes RegistryATI’s Patient Outcomes Registry has been evaluated and accepted into the federal Agency for Healthcare Research Quality’s AHRQ) Registry of Patient Registries. This is a unique honor and distinction as no other physical therapy organization has ever accomplished this. AHRQ’s mission is to produce evidence to make healthcare safe, higher quality, more accessible, equitable, and affordable. AHRQ works within the U.S. Department of Health and Human Services with other partners to make sure that the evidence is understood and used. Our Registry was also submitted, evaluated, and accepted in U.S. National Institutes of Health’s ClinicalTrials.gov.We currently can query over 800 variables in our Registry.

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Registries-a-go-go

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So, evidence-based practice ROCKs!

Right…?

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Half of what is taught in medical school will be wrong in 10 years’ time, the problem is we don’t know which half.

Sydney Burwell, MD, former Dean,

Harvard Medical School

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It took an average of 17 years for new knowledge generated by RCTs to be incorporated into practice.

–IOM

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Not a problem of too little,

but too much

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• 3600 statistical articles are published on average each year

• Do you know how long it would take you to keep up…?

Just for Coronary Heart Disease…

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Anyone…?

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If you read 1 article/15 minutes

You would have to read >10 articles

For 2 hours/day

7 days/week

Forever…

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OK,So, now WHAT?

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>15,000 prior-managed bills were loaded and rerun against the ODG Treatment UR

Advisor for each ICD9-CPT combination on frequency, number of visits,

recommendations from ODG Treatment, and the "Bill Review Payment (or ODG Approval)

Flags" divided into Green, Yellow, Red…

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Green, OK to auto-pay up to ODG Codes for Automated Approval max number of visits;Yellow, OK to auto-pay up to 25th %tile number of visitsRed, need to review

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Apple HealthKit

In 14 of 23 major hospitals are trialing (Google and Samsung discussing health-based technology plans)

Healthcare + fitness apps = comprehensive picture

Send to MD or case manager

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…yes, you guessed it, there is also…

And, it’s not just Kinect for rehab…

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  Evidence-Based Medicine

Precision Medicine

Model General Specific Sample Large cohorts “N of 1” but a massive

number of data-points/inputs

Decisions Infer recommendations applied to all thus “one

size fits all”

Limited to no generalizability

Outliers Ignored NoneStatistical

PowerGood Poor

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Can be viewed as mutually complementary. Merging the strengths of both will be based on our capacity to

perform deep investigations of large cohorts of patients. The conversion from single cases to an evidence-based

approach will imply collation and meta-analyses of big data from cross-institutional and transnational large-scale registers and cohorts.

“N-of-one” cases to an “N-of many” paradigm.

Reconciling evidence-based medicine and precision medicine*

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This transition to an “evidence-based precision medicine” will, however, necessitate standardization as well as responsible sharing and mutualizing across numerous interoperable data warehouses.

Following the same rationale, the numerous registries and large biobanks that are assembled all over the world should also be constructed in such a way as to warrant this need for inter-operability.

* Beckmann, JS & Lew, D. (2016). Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities, Genome Medicine20168:134. DOI: 10.1186/s13073-016-0388-7

Reconciling evidence-based medicine and precision medicine*

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We are in the midst of some wonderfully revolutionalry and

promising changes afoot…

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Please be in [email protected] or visit DrChrisStout.com for these slides and references

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All disclosures up to date on AAOS None relevant to this topic

Disclosures