Boston Strategic Partners, Inc. 4 Wellington St. Suite 3 Boston, MA 02118 Electronic Health Record...
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Transcript of Boston Strategic Partners, Inc. 4 Wellington St. Suite 3 Boston, MA 02118 Electronic Health Record...
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