BACKGROUND: The digital transformation of medical data presents opportunities for novel approaches to manage patients with persistent hypertension. We sought to develop an actionable taxonomy of patients with persistent hypertension (defined as 5 or more consecutive measurements of blood pressure ≥160/100 mmHg over time) based on data from the electronic health records. METHODS: This qualitative study was a content analysis of clinician notes in the electronic health records of patients in the Yale New Haven Health System. Eligible patients were 18 to 85 years and had blood pressure ≥160/100 mmHg at 5 or more consecutive outpatient visits between January 1, 2013 and October 31, 2018. A total of 1664 patients met criteria, of which 200 records were randomly selected for chart review. Through a systematic, inductive approach, we developed a rubric to abstract data from the electronic health records and then analyzed the abstracted data qualitatively using conventional content analysis until saturation was reached. RESULTS: We reached saturation with 115 patients, who had a mean age of 68.1 (SD, 11.6) years; 54.8% were female; 52.2%, 30.4%, and 13.9% were White, Black, and Hispanic patients. We identified 3 content domains related to persistence of hypertension: (1) non-intensification of pharmacological treatment, defined as absence of antihypertensive treatment intensification in response to persistent severely elevated blood pressure; (2) non-implementation of prescribed treatment, defined as a documentation of provider recommending a specified treatment plan to address hypertension but treatment plan not being implemented; and (3) non-response to prescribed treatment, defined as clinician-acknowledged persistent hypertension despite documented effort to escalate existing pharmacologic agents and addition of additional pharmacologic agents with presumption of adherence. CONCLUSIONS: This study presents a novel actionable taxonomy for classifying patients with persistent hypertension by their contributing causes based on electronic health record data. These categories can be automated and linked to specific types of actions to address them.
Background Office-based opioid treatment (OBOT) is an evidence-based treatment model for opioid use disorder (OUD) offered by both addiction and general primary care providers (PCPs). Calls exist for more PCPs to offer OBOT. Few studies have been conducted on the primary care characteristics of OBOT patients. Objective To characterize medical conditions, medications, and treatment outcomes among patients receiving OBOT with buprenorphine for OUD, and to describe differences among patients by age and by time in care. Methods This study is a retrospective review of medical records on or before 4/29/2019 at an outpatient primary care clinic within a nonprofit addiction treatment setting. Inclusion criterion was all clinic patients actively enrolled in the OBOT program. Patients not prescribed buprenorphine or with no OBOT visits were excluded. Results Of 355 patients, 42.0% had another PCP. Common comorbid conditions included chronic pain and psychiatric diagnosis. Few patients had chronic viral hepatitis or HIV. Patients reported a median of 4 medications. Common medications were cardiovascular, antidepressant, and nonopioid pain agents. Older patients had a higher median number of medications. There was no significant difference in positive opioid urine toxicology (UT) based on age, chronic pain status, or psychoactive medications. Patients retained >1 year were less likely to have positive opioid UT. Conclusion Clinical needs of many patients receiving OBOT are similar to those of the general population, supporting calls for PCPs to provide OBOT.
Introduction: Adults living with sickle cell disease (SCD) have high utilization of emergency departments (ED) and hospitals, and utilization typically is skewed with a subgroup with very high utilization. We sought to promote a shift from ED/hospital utilization to ambulatory care (Amb) services in our state through ED/hospital visit-prompted communication with ED/hospital and Amb practitioners. Methods: The Community Health Network of Connecticut, Inc. (CHNCT) is the medical administrative services organization (contractor) for the physical health benefit of the state's Medicaid Program, HUSKY Health. Among HUSKY members ages 16 years and older (16+) with Medicaid-only insurance and with at least one Medicaid payment claim with a primary or secondary ICD-9/ICD-10 diagnosis of sickle cell disease from Feb 28, 2016 thru Feb 25, 2017, we identified high ED/hospital utilizing members. CHNCT monitored high utilizing 16+ member ED/hospital visits in real time using admission, discharge, and transfer data, and provided practitioner team members with individual member utilization data (mUD) to be shared via telephone contact with ED/hospital and Amb practitioners when 16+ members visited an ED or were hospitalized. Shared mUD included numbers of ED/hospital visits, names of ED/hospital facilities visited, and information about Amb services visits. ED/hospital practitioners were encouraged to advise members to seek regular care from one of the state's hospital-based SCD programs or other practitioners. No formal hypotheses were declared for testing for statistical significance. Differences in utilization were compared with the Mann-Whitney U test. Results: We identified 705 16+ members living with SCD. In phase 1, high utilizing members (HUM1) were defined as individuals with 12+ ED visits or 8+ hospital visits in the reference year. We identified 45 (6%) HUM1 who accounted for approximately half of all 16+ member ED visits [1364 (56%) of 2436] and hospital visits [368 (47%) of 788]. Among HUM1, the distribution of visits was highly skewed with individual HUM1 accounting for up to 184 ED and up to 27 hospital visits. HUM1 used up to 27 facilities, some out-of-state. In phase 2, we identified an additional 47 members (HUM2) with 6+ ED visits or 4+ hospital visits in the reference year. From Aug 2017 to Jun 2019, CHNCT notified clinical team members of 504 ED/hospital visits involving 51 HUM, and clinical team members shared mUD with practitioners on 342 (68%) occasions. For data analysis, the HUM1 pre-information sharing period was defined as 1/1/16 thru 11/9/16; the post-information sharing period was defined as 7/11/16 thru 7/10/17. For HUM2, pre- 6/6/17 thru 6/5/18, and post- 6/6/18 thru 12/13/18. As the duration of time periods differed, data were expressed as visits (or dollars) per year. ED visit rates fell by one third for HUM1 (33%), but were little changed for HUM2 (see Table 1). Hospitalization rates fell by about one third for both HUM1 (39%) and HUM2 (32%). P values for changes in ED visit rates for HUM1 and HUM2 and in hospitalization rates for HUM2 ranged from 0.005 to 0.01. Medicaid ED and hospital expenditures per year fell more than one third (35%), or about $2.2M. Conclusions: Six percent of CT Medicaid members 16 years and older living with SCD accounted for approximately half of all those members ED and hospital utilization. Visit-prompted sharing of utilization data of these high utilizing members with ED, hospital, and Amb practitioners coupled with a recommendation to advise these members to seek regular Amb services led to decreases in ED visits, hospitalizations, and total expenditures by about one third. The same intervention applied to a cohort of the next highest utilizing members resulted in a similar change in hospitalizations but minimal change in ED visits. Medicaid expenditures for ED visits and hospitalizations for the groups combined fell by about one third, or $2.2 million per year. It will be important to ascertain whether information sharing changed utilization of Amb services. This quality improvement project is HIPAA compliant; no institutional review board approval was required. Table Disclosures Roberts: Community Health Network of Connecticut: Consultancy; Truven Health Analytics: Consultancy. Andemariam:Emmaus: Membership on an entity's Board of Directors or advisory committees; New Health Sciences: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Terumo BCT: Membership on an entity's Board of Directors or advisory committees; Global Blood Therapeutics: Other: DSMB Member; Imara: Research Funding; Cyclerion: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; NovoNordisk: Membership on an entity's Board of Directors or advisory committees; Bluebird Bio: Membership on an entity's Board of Directors or advisory committees; Community Health Network of Connecticut: Consultancy. Latham:Community Health Network of Connecticut: Employment. Cyr:Community Health Network of Connecticut: Employment. Magras:Community Health Network of Connecticut: Employment.
Background: The digital transformation of medical data presents opportunities for novel approaches to manage patients with persistent hypertension (defined as multiple measurements of elevated BP over 6 months). We sought to develop an actionable taxonomy of patients with persistent hypertension based on clinical data from the electronic health records (EHR). Methods: This qualitative study was a content analysis of clinician notes in the EHR of patients in the Yale New Haven Health System. Eligible patients were 18 to 85 years and had blood pressure ≥160/100 mmHg at five or more consecutive outpatient visits between January 1st 2013 to October 31st 2018. A total of 4,828 patients met criteria, of which 200 records were randomly selected for chart review. Through a systematic, inductive approach, we developed a rubric to abstract data from the EHR and then analyzed the abstracted data qualitatively using conventional content analysis until saturation was reached. Results: We reached saturation with 115 patients, who had a mean age of 68.1 (SD, 11.6) years; 54.8% were female; 52.2%, 30.4%, and 13.9% were White, Black, and Hispanic people. We identified three content domains related to persistence of hypertension: (1) non-intensification of pharmacological treatment (defined as absence of antihypertensive treatment intensification in response to persistent severely elevated blood pressure) with four subcategories, including provider purview, competing medical priorities, patient preference, and de-emphasis of the office measurement; (2) non-implementation of prescribed treatment (defined as a documentation of provider recommending a specified treatment plan to address hypertension but treatment plan not being implemented) with four subcategories, including obstacles to obtaining medications, psychosocial barriers, patient misunderstanding, and negative medication experience; and (3) non-response to prescribed treatment (defined as clinician-acknowledged persistent hypertension despite documented effort to escalate existing pharmacologic agents and addition of additional pharmacologic agents with presumption of adherence) with two subcategories, including resistant hypertension and secondary hypertension. Conclusions: This study presents a novel actionable taxonomy for classifying patients with persistent hypertension by their contributing causes based on EHR data. These categories can be automated and linked to specific types of actions to address them.
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