Boston Strategic Partners, Inc.4 Wellington St. Suite 3

Boston, MA 02118www.bostonsp.com

Electronic Health Record (EHR)Data Analysis Capabilities

January 2014

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Boston Strategic Partners is uniquely positioned to provide insights and value to its clients

Boston Strategic Partners is a life sciences consulting firm with preferred access to comprehensive EHR database

14+ years of real world EHR data (from 2000 to present day)

45+ years of combined analytics expertise

PhD level experience with SAS data management and biostatistics

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The dataset includes EHR data on over 36 million unique patients across 486 facilities in the United States

About Health Facts®: Health Facts® is a de-identified research database sourced by real-world patient care data from electronic health records (EHRs). Data is aggregated and mapped for participating facilities through a processthat and employs quality assurance processes to help ensure data integrity.

Health Facts® includes comprehensive clinical records comprising pharmacy, laboratory, registration and billing data from all patient care locations. All admissions, discharges, medication orders, clinical events, laboratory orders and specimen collections are time-stamped and sequenced.

With Health Facts® data, researchers can answer challenging research questions and gain deep insight into real-world practice patterns, clinical decision making and care strategies. Additionally, they can track drug usage relative to diagnoses and across geographic region and hospital type.

>36M

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Health Facts® includes the most common and often most costly conditions

Just a few of the conditions

included in Health Facts® Database

Acute coronary syndrome (ACS)

Asthma

Atrial fibrillation (AF)

Cardiovascular disease

Chronic obstructive pulmonary disease (COPD)

Congestive heart failure (CHF)

Complicated skin & skin-related structure infection

Cystic fibrosis

Diabetes

Hip and knee surgery

Hypertension

Intra-abdominal infection (IAI)

Pneumonia (including CAP)

Sepsis and bacteremia

Stroke/TIA

Venous thromboembolic event (VTE)

And many more…

Health Facts® includes a complete set of both ICD-9 diagnosis and

procedure codes

>16,000 Diagnosis Codes

>4,000 Procedure Codes

Health Facts® provides time stamped real world data on de-identified patients

Each patient’s hospital visit defined as an “episode of care”

This data allows BSP to track and evaluate patients longitudinally across their

hospital stay including clinical values, diagnosis, procedures, lab/microbiology,

medications and outcomes

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Health Facts® data includes detailed information on most aspects of the patient’s hospital visit

Patient data is grouped together into an “Encounter” and includes the following information

Facility characteristics

Patient characteristics

ER encounters as admitting source are merged

Clinical assessments including

• height/weight/BMI

• vital signs

• respiratory

Clinical characteristics, including key comorbid conditions and selected admission labs

1º and 2º diagnoses and procedure codes (ICD-9)

Supplemental diagnoses and procedures (outside inpatient encounters)

Inpatient pharmacy orders and dispensing data

General lab with or without micro (beyond admission labs)

SurgiNet (surgical suite) data, where available

Key outcomes (in-hospital mortality, LOS, total billed charges/estimated costs, 30-day readmission)

Reimbursement/billing charges

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

• EHR-level detailed clinical information, not just orders or claims

• Longitudinal tracking of patients• Date-time stamped to minute-wise

resolution

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BSP has the capability to utilize the Health Facts® data to provide unique insights to our clients

Prepare complex models utilizing industry-validated and widely accepted software like SAS

Leverage scientific /clinical expertise to apply heuristics that exclude “garbage” data points as well as identify unique trends

Identify patient populations / “drill-down” based on ICD-9 diagnostic, procedure, DRG codes, demographics, care loci / site-of-service, payor

Stratify patients not only by “administrative” codes but also by quantitative (numeric) clinical and lab values, for example, score severity of illness by APACHE II

“Step through time” to look for patterns during the patient stay, through re-admissions, or otherwise “follow” the patient over time

Identify potential loci of care where medications are administered

Leverage our in-house technical expertise to efficiently query multiple fact tables that are each on the order of gigabytes+ and 10MM-900MM+ rows

If needed, augment the team with other, in-house professionals who can extend the value of the analysis to support the creation of peer-reviewed papers/posters and drive downstream strategic initiatives

Provide easy-to-understand project outputs with explanations using level-of-language specific to the intended audience(s)

Develop a detailed and comprehensive Data Analysis Plan document prior to data analysis

Conduct analysis of baseline patient descriptors (t-test, Χ2 test, distribution histograms) including patient demographics, hospital demographics and comorbidity data

Develop propensity score (probability of receiving a specific treatment) models for patient population using logistic regression

Match patients based on their propensity score (1:1 and N:1) to control for bias and confounding that is inherent in retrospective clinical data

Apply standard clinical guidelines to develop criteria for key outcomes

Conduct in-depth analysis of clinical outcomes using multivariate analysis

Validate the statistical significance of reported outcomes using odds ratios (ORs) with confidence intervals (CIs), C statistics and receiver operating characteristic (ROC) curves

Effects of specific patient characteristics on any outcome, such as age and severity of illness can be estimated by including these parameters explicitly in the outcome models

BSP’s Capabilities Data Analysis Methodologies

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Potential applications of EHR include support for products that are in-development as well as evaluation of marketed products

Understand current practices and event rates to guide and expedite clinical trial design

Analyze unmet needs by understanding a patient’s journey from admission to discharge

Understand how health care is delivered today over a longitudinal basis

Understanding the economics of healthcare Analyze outcomes

“In Development” Analysis

Analyze sequences of events for patients of interest leveraging time stamped data

Identify initial treatment prescribed and subsequent modifications to treatment over the length of stay

Identify adverse events defined by treatment or labs

• Interactions between concomitant medications

• Interactions between comorbid clinical conditions

Ability to leverage rich clinical data for severity-of-illness adjustment

“On Market” Analysis

TO SUPPORT

R&D / Medical Affairs Clinical trial design

• Patient selection• Event rate assessment• Comparative effectiveness

evidence generation Better understanding of disease and

care pathways Improved post-marketing surveillance

capability Clinical messaging Meta-analyses and publications Understanding how drugs are actually

utilized in “real world” scenarios (including off-label use)

Health Economics Health economic modeling:

translation of proposed clinical differentiation into economic outcomes to support pricing: budget impact models and cost effectiveness analyses

Improve coverage and payments

Commercial/ Marketing Value proposition identification and

quantification Prescriber and/or patient

segmentation• E.g. lines of therapy

Forecasting Pricing studies Competitive positioning Life cycle management

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EHR data mining provides a wealth of information that can help modernize the research process, create more efficient clinical trials and improve the results of marketing efforts

EHR-based analysis can improve efficiency, reduce costs and provide a competitive advantage in post-marketing surveillance and clinical trial cycle time

Source: Evans, Stemple: Electronic Health Records and the Value of Health IT. Journal of Managed Care Pharmacy, 14:6, S-c, 2008.

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The average delay from missing enrollment deadlines is about 90 days for which each day of delay costs an estimated $1.3 million in lost sales

Example: Slightly altering the inclusion / exclusion criteria could dramatically increase the patient population and in turn help further reduce recruitment cycle times

In addition, prior clinical and diagnostic data could help improve clinical trial design through a more comprehensive understanding of disease progression and care pathways

Source: 3rd Annual Merging Electronic Health Records and eClinical Technologies: Leveraging EHRs for Clinical Research—Considerations for tomorrows technology, Sept 24-25, Annapolis

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Health Facts® data includes detailed data on most aspects of the patients hospital visit

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

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Facility Type Data

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

Facilities Type data includes: US Census Region and Division (all

represented) Bed size category Teaching status Urban/rural community setting Part of hospital system Acute care status Cardiac cath lab status

• Full cath lab • Diagnostic cath lab only

Statistically derived cost-to-charge ratio available

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Admissions and Discharge Date

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

Admissions and Discharge data includes: Type of encounter Physician specialty Date/time of admission Admission type (e.g. elective) Admission source Date of discharge Discharge disposition

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Patient Demographics Data

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

Patient Demographics data includes: Patient identifier Age at admission Race Gender Payer

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Clinical Assessment Data

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

Clinical Assessment data includes: Height Weight Blood pressure Heart rate Pulse Temperature LOC / Glasgow Coma Scale BMI

Respiratory rate Smoking status Alcohol use Pregnancy status Allergies Apgar Symptoms Pain assessment

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Diagnosis and Procedure Data

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

Diagnosis and Procedure data includes: 1º and 2º diagnoses and procedure codes

(ICD-9) Supplemental diagnoses and procedures

(outside inpatient encounters) Discharge diagnoses, procedures

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General Labs Data

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

General Labs data includes: Procedure name Specimen source and type Date / time of order, collection, and

completion Result and unit of measure Type of result Normal ranges, if applicable Ordering physician specialty Treatment setting

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Microbiology Susceptibility Data

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

Microbiology Susceptibility data includes: Procedure name Positive isolate name Anti-microbial used for testing Result Specimen source Date / time of order Date / time of collection Date / time of performance Date / time of verification

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Medications Data

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

Medication data includes: Drug name Drug class Ordering physician specialty Treatment setting (on order) Dose, rout of administration Order frequency Total quantity dispensed Start and discontinue dates

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Billed Charges Data

Facility Type

Patient Demographics

Labs and Microbiology

Medications

Admission and Discharge

Clinical Assessment

Billed Charges

Diagnosis and Procedure

Billed Charges data includes: Total billed charges Cost to charge ratios