Socio-behavioral determinants of health (SBDH) Data Catalog
Socio-behavioral determinants of health (SBDH) – a combination of behavioral, social, economic, environmental, and occupational factors – are powerful drivers of morbidity, mortality, and future well-being of individuals and communities, yet they mostly lie outside domain of the conventional medical care delivery system. Modifiable behaviors and exposures that occur in the community play a significant role in 60% of preventable deaths in the U.S.1

Social and Non Clinical Risk Factors2
Project Executive Summary
In 2017, the Institute for Clinical and Translational Research’s (ICTR) Behavioral, Social and Systems Science (BSSS) Translational Research Community (TRC) advisory board funded this project to examine the availability of social and behavioral data in JHMI’s EPIC EMR/PHR systems. Both researchers and administrators recognize that patients’ socio-behavioral determinants play a critical role their care experiences and outcomes. Being located in Maryland with its global budgets and population-based reimbursement scheme, it is advantageous for JHU/JHMI to find cost-effective, community-level solutions that improve the population’s health status. The vision of the BSSS TRC board is to enable JHU researchers to utilize social and behavioral data collected from JHMI patients and stored in various data sources such as EPIC.
EHR data (i.e., Epic) will play an instrumental role in population health management efforts of value-based providers such as JHMI[3-9]. EHR-extracted socio-behavioral determinants data can potentially help to coordinate care and risk stratification efforts [10]. However, certain challenges will remain while extracting socio-behavior factors from JHMI’s Epic such as: data quality issues and missing socio-behavioral data [11-12], ongoing immaturity of EHR’s advanced functionality across various providers [13], and the need for complex methods to extract socio-behavioral determinants from EHR’s unstructured text [14].
Given the increasing alignment of population and public health efforts [15-18], identifying socio-behavioral factors of high-risk JHMI patients will be key in addressing underlying disparities within JHMI’s population as Maryland’s all-payer waiver program is already piloting population-level outcome measures [19-20]. Using non-Epic data sources, such as CRISP information (i.e., Maryland’s health information exchange), can also be used to extract socio-behavioral determinant data [21].
For the purposes of this project, Epic is the sole data source. No legacy or ancillary systems were queried. As of 7/1/2018, there are approximately 5.4 million unique patients in Epic. The contents of this page will be reviewed and updated regularly.
Data Availability Timeline
The following diagram depicts the approximate timeline for when data was migrated into Epic or when Epic began capturing data, segmented by data type and facility.
Data Variables
- Address/Zip Code
- Alcohol Use
- Ethnicity
- Income/Financial Issues
- Housing Issues
- Language
- Race
- Smoking Status
- Social Support
Address/Zip Code
Most Common Collection Method
The most common method to collect patient permanent address is upon registration of each encounter. Address is defined as a street address/number, an optional line for apartment or other information, a city, a state or province, and a zip code.
General Completeness Rate
95% – out of approximately 5.4 million patients, 5.2 million patients have indicated a permanent address using this collection method.
Collection Date Range
Permanent address was migrated into Epic beginning in 2003 and is currently collected.
Facility Type (Inpatient or Outpatient)
Permanent address is collected at all facilities at the time of registration.
Address History
In 66% of the 5.4 million patients in Epic, there are address change records available. Effective start and end dates are included to track address changes over time. Address change records were first collected in 2003 and are currently tracked.
Other Collection Methods
In addition to the most commonly collected method, patient address is captured in the following cases:
Collection Method | Completeness Rate | Facility/Provider | Dates of Collection |
---|---|---|---|
Hospital Account | 73% of the 7.3 million hospital accounts | Inpatient facilities, primarily in imaging and PT services at Howard County General and Sibley Memorial Hospital. | 2013 – current |
Temporary Address | .3% out of 5.4 million patients | All facilities at the time of registration | 2003 – current |
Billing Address | 99% of 2.1 million patient accounts where the guarantor is the patient | All facilities at the time of registration | 2003 – current |
Claims Processing Address | 100% of 4.9 million claims where the patient was listed | 2013 – current | |
Insurance Coverage Address | 92% of 3.1 million coverage records where the subscriber is also the patient | All facilities at the time of registration | 2006 – current |
Home Health Encounters and Episodes | 99% of the 41,604 home care encounters | At the time of the Home Health scheduling encounter | September 2015 – current |
MyChart Patient Proxy Addresses | 79% of the 30,410 MyChart proxy records | MyChart proxies are MyChart users who log in to MyChart on behalf of a patient. In this case, proxies are not patients. Note that address is not complete for all records. | |
Communications for Specific Encounters | 100% of the 747,616 encounter-based communications | Clinicians have the ability in Epic to track communications associated with specific encounters. The most common provider types are physicians and medical assistants. | 2013 – current |
Social Work Assessment Questionnaire: Primary Caregiver Address Question | 7% of the 14,129 times that the questionnaire was completed, address was provided. | Sibley and Bayview inpatient emergency services, by social workers | July 2014 – current |
Social Work Assessment Questionnaire: Patient Address | 7% of the 14,129 times the template was used and the question was answered | This questionnaire focuses on the patient’s social, economic, safety, and psychological well-being. Captured at inpatient admissions by social workers. | June 2014 – current |
Maltreatment Assessment: Father’s address/Mother’s address | 7% of the 14,129 times the template was used and the question was answered | JHH Pediatric ED, other pediatric clinics, by social workers. | April 2013 – current |
Alcohol Use
Most Common Collection Method
Alcohol use is captured in the Social History portion of Epic during a patient encounter, whether in-person or non in-person encounters (telephone, MyChart, documentation). For the purposes of this data catalog, the variable of focus is “Alcohol Drinks Per Week”.
Collection Date Range
Social History has been collected since April 2013 and is currently collected.
General Completeness Rate
Of the approximately 5.4 million patients, 490,348 (.09%) patients reported having any value (including 0 alcoholic drinks per week) in social history. 178,789 (.03%) reported having one or more alcoholic drinks per week.
Facility Type (Inpatient or Outpatient)
Alcohol use is collected at all facilities at the time of an encounter.
Other Collection Methods
For future revisions of this data catalog, alcohol use data collected from flowsheets, questionnaires, and clinical notes will be included.
Ethnicity
Epic captures a patient’s ethnicity separate from a patient’s race, and ethnicity is defined as either Hispanic or Not Hispanic.
Most Common Collection Method
The most common method to collect patient ethnicity is upon registration of each encounter.
General Completeness Rate
50% – out of approximately 5.4 million patients, 2.7 million patients have indicated ethnicity using this collection method.
Collection Date Range
Ethnicity has been collected in Epic since 2003 and is currently collected.
Facility Type (Inpatient or Outpatient)
Ethnicity was migrated into Epic beginning in 2003 and is currently collected.
Other Collection Methods
In addition to the most commonly collected method, ethnicity is captured in the following cases:
Collection Method | Completeness Rate | Facility/Provider | Dates of Collection |
---|---|---|---|
Transplant Organ Donors | 50% of the 4,647 human transplant donors in Epic | August 1996 – present | |
Ethnicity Questionnaire | In 20% of the 2,471 times that the questionnaire was completed, the question “What is your race/ethnicity” was answered. The choices for selection are “Black”, “Hispanic”, and “Neither Black Nor Hispanic”. | Inpatient encounters at Johns Hopkins Hospital by registered nurses and case workers. | Between January 2016 and October 2016 |
Ethnicity Origin Questionnaire | In 5% of the 3,558 times that the questionnaire was completed, the questions “Father’s Ethnic Origin” and “Mother’s Ethnic Origin” were answered. The choices for selection are:
|
Two ophthalmology clinics by technicians | Between April 2013 and September 2017 |
Pregnancy/Delivery Episodes: Father’s Ethnicity | .1% of the 109,574 combined pregnancy episodes
The choices for selection are:
|
Two JHCP OB/GYN outpatient clinics | 2015 – current |
Income/Financial Issues
Patients with income/financial issues are patients in deteriorated financial status, financial hardship, or in poverty. They are unable to afford the basics of life and/or medical interventions. They are in need and eligible for any benefit or enrolment in financial assistant programs.
Collection Methods
In order to determine the population and distribution of patients having income/financial issues, several collection methods were utilized.
Diagnosis Codes (ICD-10)*
Searching Epic for patients having ICD-10 coded diagnoses on the problem list, billing codes, or recorded at the time of an encounter yielded the following results:
ICD-10 Code | Patient Count |
---|---|
Z59.7 Insufficient Social Insurance and Welfare Support | 46 |
Z59.8 Other Problems Related to Housing and Economic Circumstances | 3,357 |
Z59.5 Extreme Poverty | 68 |
Z59.6 Low Income | 72 |
* Patients with ICD-9 coded diagnoses are also included in this query. The timeframe is January 1, 2003 through June 26, 2018.
Clinical Notes
Using Natural Language Processing (NLP) techniques, keyword phrases indicating specific SDH were extracted from 23 million clinic notes authored by specific provider types between July 1, 2016 and May 31, 2018. The notes represent 1,188,202 unique patients and 9,066,508 unique encounters.3
We pre-processed the notes to mark the line boundaries and remove non-Unicode characters. To identify notes containing SDH, we used hand-crafted linguistic patterns developed by a team of experts based on SDH descriptions in LOINC, SNOMED, ICD10, Public Health Surveys and Instruments (ACS, American Housing Survey, NHANE, etc.), phrases from the literature review and other studies, and manual tagging.
To craft the linguistic patterns, the expert team focused on three domains: Housing, Finance, and Social Support.
Findings for Financial Issues:
- Of the 1,188,202 unique patients, 1% have at least one note containing mentions of financial issues.
- Of the 20,219 notes containing mentions of financial issues, about 52% were authored by physicians, 37% were authored by social workers, 8% were authored by nurses, 3% were authored by case managers and case coordinators.
- The top note types for mentions of social support are progress notes (46%), H&P notes (8%), treatment plans (6%), consults (6%), with the remaining note types dispersed among committee review, patient instructions, discharge summaries, and plan of care notes.
Housing Issues
Housing issues are categorized to those related to housing instability or insecurity, homelessness, and characteristics of the house and a number of sub-categories as follows:
- Housing Instability
- Having a House with Problems
- Prone to Homelessness
- Homelessness
- Currently Homeless
- Homelessness Being Addressed
- Housing Characteristics
- Quality of Building
- Characteristics of Building
- Building Environmental Health Hazards
- Air Quality (Including Mold)
- Infestation
- Old Paint
- Hazardous Material and Fire Protection
- Problems with House Amenities
- Water
- Sewage and Disposal
- Fuel and Heating/ Cooling System
- Electricity and Internet
- Age of Building
- Quality Problems Being Addressed
Collection Methods
In order to determine the population and distribution of patients having a housing insecurity situation, several collection methods were utilized.
Diagnosis Codes (ICD-10)*
Searching Epic for patients having ICD-10 coded diagnoses on the problem list, billing codes, or recorded at the time of an encounter yielded the following results:
ICD-10 Code | Patient Count |
---|---|
Z59.0 (Homelessness) | 7,022 |
Z59.1 (Inadequate Housing) | 120 |
Z59.8 (Other problems related to housing and economic circumstances) | 3,291 |
* Patients with ICD-9 coded diagnoses are also included in this query. The timeframe is January 1, 2003 through May 31, 2018.
Clinical Notes
Using Natural Language Processing (NLP) techniques, keyword phrases indicating specific SDH were extracted from 23 million clinic notes authored by specific provider types between July 1, 2016 and May 31, 2018. The notes represent 1,188,202 unique patients and 9,066,508 unique encounters.3
We pre-processed the notes to mark the line boundaries and remove non-Unicode characters. To identify notes containing SDH, we used hand-crafted linguistic patterns developed by a team of experts based on SDH descriptions in LOINC, SNOMED, ICD10, Public Health Surveys and Instruments (ACS, American Housing Survey, NHANE, etc.), phrases from the literature review and other studies, and manual tagging.
To craft the linguistic patterns, the expert team focused on three domains: Housing, Finance, and Social Support. We delved into the housing domain by created patterns for its subdomains, including homelessness, prone to homelessness, homelessness being addressed, housing instability, housing characteristics – characteristics of the building, housing characteristics – the quality of the building.
Findings:
- Of the 1,188,202 unique patients, 3% have at least one note containing mentions of housing issues.
- Of the 108,439 notes containing mentions of housing issues, about 53% were authored by physicians, 32% were authored by social workers, 10% were authored by nurses, 5% were authored by case managers.
- The top note types for mentions of housing issues are progress notes (46%), ED provider notes (13%), H&P notes (8%), with the remaining note types dispersed among discharge summaries, consults, and plan of care notes.
- Manual annotation of 100 randomly selected notes containing the phrase “homeless” revealed:
- Out of 100 notes, there are 130 mentions of the word “homeless”.
- In 2 out of 100 notes, there were conflicting true positives and false positives within the same note.
- In 20 out of 100 notes, there were true negatives (the note was derived from a SmartPhrase), and the answer to Homeless Y/N was No.
- From the remaining notes, 64% were true positives, and 14% were false positives. The other notes were true negatives or unclear.
- The following numbers show the count of notes containing specific variables from the housing subdomains:
- Homelessness Being Addressed: 82
- Housing Instability: 5,653
- Housing Characteristics – Characteristics of the Building:148
- Housing Characteristics – Quality of the Building: 2,011
Questionnaires
Several questionnaires exist in Epic that capture data related to housing issues. Each is described briefly below, including frequency, general completeness rate, and facility type where the questionnaire answers were completed. Please note that data provided on general completeness reflects questions that were available to an Epic user and were answered.
Questionnaire template | Question | Completeness Rate | Facility/Provider | Dates of Collection |
---|---|---|---|---|
Housing/Utility Voucher-Moore Clinic | Housing Assistance Provided? | Of the 217 times the times that the questionnaire was completed, 97 (44%) indicated that housing financial assistance was awarded. | Bartlett HIV Clinics by social workers and case managers | March 2017 – current |
Abuse/Neglect Screen | Homeless | Of the 12,058 times that the questionnaire was completed, 96% answered the question of homelessness (yes or no). | The top four facilities that answer the question are the infusion center at Sibley Memorial Hospital, the Breast Clinic at Howard County General Hospital, Physical Therapy at Howard County General Hospital, and the Howard County General Wound Center. The top providers completing the form at these facilities are registered nurses, physical therapists, and wound ostomy and continence nurses. | June 2013 – current |
Screening, Brief Intervention, and Referral to Treatment (SBIRT) Social History Questionnaire | Need Help Finding Housing? | Of the 1,900 times that the questionnaire was completed, 96% answered the question (yes or no). | The top two facilities that answer the question are Bayview Emergency Services and the BMC Chemical Dependency Unit. Peer recovery coaches complete this question. | July 2017 – current |
ED Triage Abuse Indicators and Resource Planning | Patient indicators, resource planning, and outcomes for shelter, transportation, and clothing | Of the 713,702 times that the questionnaire was completed, 5.5% answered the questions. | Inpatient, mostly in emergency units, by registered nurses. | May 2013 – current |
Sibley Chemical Dependence Unit Admission Screen | Homeless | Of the 15,056 times that the questionnaire was completed, 15% answered the question. | The top facility that answers the question is the Sibley Memorial Hospital Clinical Decision Unit, by registered nurses. | July 2014 – present |
Ambulatory Priority Access Primary Care (PAPC) Screen | What is Your Housing Situation? | Of the 1,116 times that the questionnaire was completed, 7% answered the question. | JHCP Internal Medicine EBMC is the only facility that captured this information, by physicians and medical assistants | April 2015 – May 2017 |
Adult Admission General Intake Form | Homeless | Of the 77,230 times that the questionnaire was completed, 35% answered the question (yes/no). | Top facilities where the template was used are inpatient units at Sibley Memorial Hospital and Howard County General Hospital, by registered nurses. | May 2013 – April 2016 |
Pediatric/Newborn General Intake Form | Homeless | Of the 1,067 times that the questionnaire was completed, 55% answered the question (yes/no). | Top facilities are inpatient units at Johns Hopkins Hospital and Bayview Medical Center, by social workers. | The form was not widely used until 2016. It is currently used. |
JHM Psychiatry Social Work Assessment | Living Arrangement | Of the 4,913 times that the questionnaire was completed, 90% answered the question. | The top facility is BMC inpatient Psychiatry, by social workers. | September 2015 – current |
Language
Most Common Collection Method
Patient preferred language is captured in Epic at the time of admission.
Collection Date Range
Preferred language was migrated into Epic beginning in 2003 and is currently collected.
General Completeness Rate
Of the approximately 5.4 million patients, 2,718,416 patients (49%) have indicated a preferred language in Epic. Of those indicating a preferred language, 2,624,122 indicated English as their preferred language.
The top 7 preferred languages, by unique patient count are as follows:
- No language reported (null) – 2,804,973 (51%)
- English – 2,626,379 (48.5%)
- Spanish – 53,446 (1%)
- Arabic – 7,317 (.1%)
- Unknown (a valid value in Epic, different from an empty record) – 5,936 (.1%)
- Chinese (Mandarin) – 4,036 (.1%)
- Korean – 3,168 (.1%)
Facility Type (Inpatient or Outpatient)
Preferred language is collected at all facilities at the time of an encounter.
Other Collection Methods
For future revisions of this data catalog, preferred language collected from flowsheets, questionnaires, and clinical notes will be included.
Race
Most Common Collection Method
The most common method to collect patient race is upon registration of each encounter. Patients can select multiple races to self-identify:
-
-
-
- American Indian or Alaska Native
- Asian
- Black or African American
- Declined to Answer
- Native Hawaiian or Other Pacific Islander
- Other
- Unknown
- White or Caucasian
-
-
General Completeness Rate
90% – out of approximately 5.4 million patients, 4.9 million patients have indicated at least one race using this collection method.
Collection Date Range
Race was migrated into Epic beginning in 2003 and is currently collected.
Facility Type (Inpatient or Outpatient)
Race is collected at all facilities at the time of registration.
Other Collection Methods
In addition to the most commonly collected method, race is captured in the following cases:
Collection Method | Completeness Rate | Facility/Provider | Dates of Collection |
---|---|---|---|
Home Health | 84% of the 40,301 Home Health episodes in Epic. | For Home Health encounters only, patients can select multiple races to self-identify. The selections differ from the most common collection method:
|
2004 – current |
BCRA Race | .05% of the 113 million BCRA encounters | Race is also collected at outpatient and inpatient encounters at breast imaging, ultrasound, and MRI clinics throughout the institution.
One race per patient is selected, and race selection is restricted to the following:
|
May 2013 – current |
Transplant Organ Donors | 44% of the 4,602 organ donors | Multiple races are captured for each donor from the same selections as available during the patient registration process. | 1996 – current |
Race Questionnaire | 7.4% of the 1,748 times the questionnaire was used at an HIV clinic encounter | Race is documented for patients seen in the JHH HIV Clinics, specifically collected by social workers and case managers. Only one race can be selected. Selections are restricted to the following:
|
April 2013 – current |
Smoking Status
Most Common Collection Method
Smoking status is captured in the Social History portion of Epic during a patient encounter, whether in-person or non in-person encounters (telephone, MyChart, documentation). For the purposes of this data catalog, the variable of focus is “Smoking Status”.
Collection Date Range
Smoking status has been collected since April 2013 and is currently collected.
General Completeness Rate
Of the approximately 5.4 million patients, 1,728,749 patients (32%) reported having any value smoking status in social history. Smoking Quit Date is also populated, but only in 137,958 (3%) of the time. The status breakdown, with collection rate, is as follows:
Smoking Status selection options include:
- Current Every Day Smoker (114,566 – 2.1%)
- Current Some Day Smoker (28,547 – .5%)
- Former Smoker – (297,099 – 5.5%)
- Heavy Tobacco Smoker (3,111 – .1%)
- Light Tobacco Smoker (12,857 – .2%)
- Never Assessed (302,631 – 5.6%)
- Never Smoker – (952,636 – 17.7)
- Passive Smoke Exposure – Never Smoker – (4,274 – .1%)
- Smoker, Current Status Unknown – (1,133 – < .01%)
- Unknown if Ever Smoked – (11,915 – < .2%)
Facility Type (Inpatient or Outpatient)
Smoking status is collected at all facilities at the time of an encounter.
Other Collection Methods
For future revisions of this data catalog, alcohol use data collected from flowsheets, questionnaires, and clinical notes will be included.
Social Support
Lack of Social Support (Social Isolation) or At Risk for Social Isolation
Referred to patients in deteriorated aloneness state with lack of interaction with others, feel lonely, detached, and isolated with no help and support system. They have restricted social participation and can’t maintain social relationships. They are unable to communicate with others and have difficulty visiting friends, attending clubs, meetings, and going to parties. They might be the target of perceived adverse discrimination or persecution.
Collection Methods
In order to determine the population and distribution of patients lacking social support, several collection methods were utilized.
Diagnosis Codes (ICD-10) *
Searching Epic for patients having ICD-10 coded diagnoses on the problem list, billing codes, or recorded at the time of an encounter yielded the following results:
ICD-10 Code | Patient Count |
---|---|
R45.8 Other symptoms and signs involving emotional state | 3,340 |
Z60.4 Social Exclusion and Rejection | 223 |
Z60.2 Problems related to living alone | 1,222 |
Z63.0 Problems in relationship with spouse or partner | 852 |
Z63.5 Disruption of family by separation and divorce | 548 |
Z63.8 Other specified problems related to primary support group | 2,230 |
Z63.9 Problem related to primary support group, unspecified | 3,247 |
Z65.9 Problems related to unspecified psychosocial circumstances | 938 |
Z73.4 Inadequate social skills, not elsewhere classified | 81 |
Z91.89 Other specified personal risk factors, not elsewhere classified | 18,947 |
* Patients with ICD-9 coded diagnoses are also included in this query. The timeframe is January 1, 2003 through May 31, 2018.
Clinical Notes
Using Natural Language Processing (NLP) techniques, keyword phrases indicating specific SDH were extracted from 23 million clinic notes authored by specific provider types between July 1, 2016 and May 31, 2018. The notes represent 1,188,202 unique patients and 9,066,508 unique encounters.3
We pre-processed the notes to mark the line boundaries and remove non-Unicode characters. To identify notes containing SDH, we used hand-crafted linguistic patterns developed by a team of experts based on SDH descriptions in LOINC, SNOMED, ICD10, Public Health Surveys and Instruments (ACS, American Housing Survey, NHANE, etc.), phrases from the literature review and other studies, and manual tagging.
To craft the linguistic patterns, the expert team focused on three domains: Housing, Finance, and Social Support.
Findings for Social Support:
- Of the 1,188,202 unique patients 2.6% have at least one note containing mentions of social support.
- Of the 63,185 notes containing mentions of social support, about 70% were authored by physicians, 15% were authored by nurses, 13% were authored by social workers, 2% were authored by case managers and case coordinators.
- The top note types for mentions of social support are progress notes (46%), H&P notes (8%), treatment plans (6%), consults (6%), with the remaining note types dispersed among committee review, patient instructions, discharge summaries, and plan of care notes.
Questionnaires
Several questionnaires exist in Epic that capture data related to social support. Each is described briefly below, including frequency, general completeness rate, and facility type where the questionnaire answers were completed. Please note that data provided on general completeness reflects questions that were available to an Epic user and were answered.
Questionnaire template | Question | Completeness Rate | Facility/Provider | Dates of Collection |
---|---|---|---|---|
Nursing Assessment – Psychosocial | Psychosocial (WDL) | Of the 1,026,988 times that the questionnaire was completed, 92% answered the question. | Top units are inpatient units at Howard County General and Suburban Hospital by registered nurses, licensed practical nurses, and case managers | May 2013 – current |
ED Assess Head to Toe | Psychosocial (WDL) | Of the 237,143 times that the questionnaire was completed, 39% answered the question. | The top site is the inpatient SH Clinical Decision Unit, Suburban Hospital by registered nurses | October 2013 – current |
T AD ED Nursing Assessment | Psychosocial (WDL) | Of the 217,954 times that the questionnaire was completed, 94% answered the question. | The top units are emergency medicine inpatient units at Johns Hopkins Hospital and Bayview Medical Center by registered nurses | December 2015 – current |
T CD ED NURSING ASSESSMENT | Psychosocial (WDL) | Of the 278,084 times that the questionnaire was completed, 61% answered the question. | The top units are emergency units at Howard County General Hospital, Sibley Memorial Hospital, and Suburban Hospital by registered nurses | December 2015 – current |
T AD/CD ED PEDS ASSESSMENT | Psychosocial (WDL) | Of the 131,134 times the questionnaire was completed, 71% answered the question. | The top units are pediatric emergency departments at Johns Hopkins Hospital, Bayview Medical Center, Sibley Memorial Hospital, and Howard County General Hospital by registered nurses | December 2015 – current |
T JHM OR AD PACU FLOWSHEET | Psychosocial (WDL) | Of the 147,694 times the questionnaire was completed, 56% answered the question. | Inpatient post-anesthesia units at Johns Hopkins Hospital by registered nurses | September 2015 – current |
JHM IP OT NEW HOME SETUP | Social support available at discharge | Of the 131,948 times the questionnaire was completed, 36% answered the question. | Inpatient orthopedic and surgical floors at Bayview Medical Center and Suburban Hospital by occupational therapists | June 2015 – current |
IP OB POSTPARTUM | Recent loss, or change in status? (Includes loss of social status, job, divorce, death, demotion, etc.) / Has multiple friends/family members with several close confidants? | Of the 135,587 times the questionnaire was completed, 89% answered the question. | The top units were Howard County General and Sibley Memorial inpatient units by registered nurses | May 2013 – current |
IP SPIRITUAL CARE INTERVENTIONS | Spiritual/Social Network | Of the 116,719 times the questionnaire was completed, 59% answered the question. | The top units are Howard County General and Sibley Memorial inpatient units by chaplains. | May 2013 |
PED SCREENING | Personal-Social / 22. Relationship with peers / Is there a recent stress on the family or child such as birth of a child, moving, divorce or separation, death of a close relative / 23. Relationship with parents / 24. Relationship with siblings / 25. Relationship with peers / Socially withdrawn-decreased interaction with others / Socially withdrawn-decreased interaction with others | Of the 144,659 times that the questionnaire was completed, 59% answered the question. | The top clinics are outpatient JHCP pediatric clinics by medical assistants, physicians, and nurse practitioners | April 2013 – current |
JHM SBIRT SOCIAL HISTORY | Marital Status / Need to improve relationships with family? / Social Network / Social Network Comments / Participation in other social activities? / Social Activities Comments | Of the 2,015 times that the questionnaire was completed, 99% answered the question. | Inpatient units at Bayview Medical Center by peer recovery coaches | July 2017 – current |
JHM ED SW SUICIDE/HOMICIDE ASSESSMENT | Suicide Risk: Single, Widowed, or Divorced / Suicide Risk: No Social Support | Of the 15,101 times that the questionnaire was completed, 97% answered the question. | The top units are inpatient units at Sibley Memorial Hospital and Suburban Hospital by social workers and therapists | June 2013 – current |
JHM ED SOCIAL WORK PATIENT INFO | Support System’s Name(s) / Support System Contact / Other Support System Information / Support System | Of the 14,481 times that the questionnaire was completed, 88% answered the question. | The top units are emergency departments at Suburban Hospital and Bayview Medical Center by social workers and therapists | July 2014 – current |
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- Hatef E, Kharrazi H, VanBaak E, Falcone M, Ferris L, Mertz K, Perman C, Bauman A, Lasser EC, Weiner JP. A state-wide health IT infrastructure for population health: building a community-wide electronic platform for Maryland’s all-payer global budget. Online J Public Health Inform. 2017; 9(3): e195
- Kharrazi H, Horrocks D, Weiner JP. Use of HIEs for value‐based care delivery: a case study of Maryland’s HIE. In Dixon B (Ed.) Health Information Exchange: Navigating and Managing a Network of Health Information Systems. 2016; 313-332. Cambridge, MA: Academic Press Elsevier
Notes
3Disclaimer about NLP findings: The reported NLP findings are based on the occurrences/mentions of specific linguistic patterns (aka, keywords or key phrases) within a given clinical note. At this stage of the project, we did not perform a manual quality check but we think occurrences of variable-specific patterns could give us a good proxy of the variable in question.