Improving pain care through implementation of the Stepped Care Model at a multisite community health center.

PURPOSE: Treating pain in primary care is challenging. Primary care providers (PCPs) receive limited training in pain care and express low confidence in their knowledge and ability to manage pain effectively. Models to improve pain outcomes have been developed, but not formally implemented in safety net practices where pain is particularly common. This study evaluated the impact of implementing the Stepped Care Model for Pain Management (SCM-PM) at a large, multisite Federally Qualified Health Center. METHODS: The Promoting Action on Research Implementation in Health Services framework guided the implementation of the SCM-PM. The multicomponent intervention included: education on pain care, new protocols for pain assessment and management, implementation of an opioid management dashboard, telehealth consultations, and enhanced onsite specialty resources. Participants included 25 PCPs and their patients with chronic pain (3,357 preintervention and 4,385 postintervention) cared for at Community Health Center, Inc. Data were collected from the electronic health record and supplemented by chart reviews. Surveys were administered to PCPs to assess knowledge, attitudes, and confidence. RESULTS: Providers' pain knowledge scores increased to an average of 11% from baseline; self-rated confidence in ability to manage pain also increased. Use of opioid treatment agreements and urine drug screens increased significantly by 27.3% and 22.6%, respectively. Significant improvements were also noted in documentation of pain, pain treatment, and pain follow-up. Referrals to behavioral health providers for patients with pain increased by 5.96% (P=0.009). There was no significant change in opioid prescribing. CONCLUSION: Implementation of the SCM-PM resulted in clinically significant improvements in several quality of pain care outcomes. These findings, if sustained, may translate into improved patient outcomes.

Stepped care model of pain management and quality of pain care in long-term opioid therapy.

Successful organizational improvement processes depend on application of reliable metrics to establish targets and to monitor progress. This study examined the utility of the Pain Care Quality (PCQ) extraction tool in evaluating implementation of the Stepped Care Model for Pain Management at one Veterans Health Administration (VHA) healthcare system over 4 yr and in a non-VHA Federally qualified health center (FQHC) over 2 yr. Two hundred progress notes per year from VHA and 150 notes per year from FQHC primary care prescribers of long-term opioid therapy (>90 consecutive days) were randomly sampled. Each note was coded for the presence or absence of key dimensions of PCQ (i.e., pain assessment, treatment plans, pain reassessment/outcomes, patient education). General estimating equations controlling for provider and facility were used to examine changes in PCQ items over time. Improvements in the VHA were noted in pain reassessment and patient education, with trends in positive directions for all dimensions. Results suggest that the PCQ extraction tool is feasible and may be responsive to efforts to promote organizational improvements in pain care. Future research is indicated to improve the reliability of the PCQ extraction tool and enhance its usability.

Development and validation of the Medical Home Care Coordination Survey for assessing care coordination in the primary care setting from the patient and provider perspectives.

BACKGROUND: Community health centers are increasingly embracing the Patient Centered Medical Home (PCMH) model to improve quality, access to care, and patient experience while reducing healthcare costs. Care coordination (CC) is an important element of the PCMH model, but implementation and measurability of CC remains a problem within the outpatient setting. Assessing CC is an integral component of quality monitoring in health care systems. This study developed and validated the Medical Home Care Coordination Survey (MHCCS), to fill the gap in assessing CC in primary care from the perspectives of patients and their primary healthcare teams. METHODS: We conducted a review of relevant literature and existing care coordination instruments identified by bibliographic search and contact with experts. After identifying all care coordination domains that could be assessed by primary healthcare team members and patients, we developed a conceptual model. Potentially appropriate items from existing published CC measures, along with newly developed items, were matched to each domain for inclusion. A modified Delphi approach was used to establish content validity. Primary survey data was collected from 232 patients with care transition and/or complex chronic illness needs from the Community Health Center, Inc. and from 164 staff members from 12 community health centers across the country via mail, phone and online survey. The MHCCS was validated for internal consistency, reliability, discriminant and convergent validity. This study was conducted at the Community Health Center, Inc. from January 15, 2012 to July 15, 2014. RESULTS: The 13-item MHCCS - Patient and the 32-item MHCCS - Healthcare Team were developed and validated. Exploratory Structural Equation Modeling was used to test the hypothesized domain structure. Four CC domains were confirmed from the patient group and eight were confirmed from the primary healthcare team group. All domains had high reliability (Cronbach's α scores were above 0.8). CONCLUSIONS: Patients experience the ultimate output of care coordination services, but primary healthcare staff members are best primed to perceive many of the structural elements of care coordination. The proactive measurement and monitoring of the core domains from both perspectives provides a richer body of information for the continuous improvement of care coordination services. The MHCCS shows promise as a valid and reliable assessment of these CC efforts.

Using electronic health records data to identify patients with chronic pain in a primary care setting.

OBJECTIVE: To develop and validate an accurate method to identify patients with chronic pain using electronic health records (EHR) data at a multisite community health center. MATERIALS AND METHODS: We identified patients with chronic pain in our EHR system using readily available data elements pertaining to pain: diagnostic codes (International Classification of Disease, revision 9; ICD-9), patient-reported pain scores, and opioid prescription medications. Medical chart reviews were used to evaluate the accuracy of these data elements in all of their combinations. We developed an algorithm to identify chronic pain patients more accurately based on these evaluations. The algorithm's results were validated for accuracy by comparing them with the documentation of chronic pain by the patient's treating clinician in 381 random patient charts. RESULTS: The new algorithm, which combines pain scores, prescription medications, and ICD-9 codes, has a sensitivity and specificity of 84.8% and 97.7%, respectively. The algorithm was more accurate (95.0%) than pain scores (88.7%) or ICD-9 codes (93.2%) alone. The receiver operating characteristic was 0.981. DISCUSSION: A straightforward method for identifying chronic pain patients solely using structured electronic data does not exist because individual data elements, such as pain scores or ICD-9 codes, are not sufficiently accurate. We developed and validated an algorithm that uses a combination of elements to identify chronic pain patients accurately. CONCLUSIONS: We derived a useful method that combines readily available elements from an EHR to identify chronic pain with high accuracy. This method should prove useful to those interested in identifying chronic pain patients in large datasets for research, evaluation or quality improvement purposes.