scholarly journals Use of Electronic Health Records for Early Detection of High-Cost, Low Back Pain Patients

2015 ◽  
Vol 20 (5) ◽  
pp. 234-240 ◽  
Author(s):  
Daniel D Maeng ◽  
Walter F Stewart ◽  
Xiaowei Yan ◽  
Joseph A Boscarino ◽  
Jack Mardekian ◽  
...  
Keyword(s):  
Primary Care ◽  
Logistic Regression ◽  
Low Back Pain ◽  
Back Pain ◽  
Care Provider ◽  
Regression Models ◽  
Low Back ◽  
Health Records ◽  
Logistic Regression Models ◽  
Electronic Health

BACKGROUND: Low back pain (LBP) is a debilitating condition that is complex to manage. One reason is that clinicians lack means to identify early on patients who are likely to become high care utilizers.OBJECTIVE: To explore the feasibility of developing a ‘dynamic’ predictive model using electronic health record data to identify costly LBP patients within the first year after their initial LBP encounter with a primary care provider. Dynamic, in this context, indicates a process in which the decision on how to manage patients is dependent on whether they are at their first, second or third LBP visit with the provider.METHODS: A series of logistic regression models was developed to predict who will be a high-cost patient (defined as top 30% of the cost distribution) at each of the first three LBP visits.RESULTS: The c-statistics of the three logistic regression models corresponding to each of the first three visits were 0.683, 0.795 and 0.741, respectively. The overall sensitivity of the model was 42%, the specificity was 86% and the positive predictive value was 48%. Men were more likely to become expensive than women, while patients who had workers’ compensation as their primary payer type had higher use of prescription opioid drugs or were smokers before the first LBP visit were also more likely to become expensive.CONCLUSION: The results suggest that it is feasible to develop a dynamic, primary care provider visit-based predictive model for LBP care based on longitudinal data obtained via electronic health records.

scholarly journals Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

Rheumatology ◽  
2021 ◽  
Author(s):  
Dahai Yu ◽  
George Peat ◽  
Kelvin P Jordan ◽  
James Bailey ◽  
Daniel Prieto-Alhambra ◽  
...  
Keyword(s):  
Primary Care ◽  
Chronic Pain ◽  
Low Back Pain ◽  
Back Pain ◽  
Electronic Health Records ◽  
High Impact ◽  
Low Back ◽  
Musculoskeletal Health ◽  
Health Records ◽  
Electronic Health

Abstract Objectives Better indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters. Methods We collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years. Results The optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years. Conclusion National and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.

Influences on Primary Care Provider Imaging for a Hypothetical Patient with Low Back Pain

2018 ◽  
Vol 111 (12) ◽  
pp. 758-762
Author(s):  
Hai H. Le ◽  
Matthew DeCamp ◽  
Amanda Bertram ◽  
Minal Kale ◽  
Zackary Berger
Keyword(s):  
Primary Care ◽  
Low Back Pain ◽  
Back Pain ◽  
Care Provider ◽  
Primary Care Provider ◽  
Low Back ◽  
Hypothetical Patient

PC0007 Inflammatory low back pain features at primary care

2013 ◽  
Vol 71 (Suppl 3) ◽  
pp. 724.4-725
Author(s):  
L. Myasoutova ◽  
S. Lapshina ◽  
M. Protopopov ◽  
S. Erdes
Keyword(s):  
Primary Care ◽  
Low Back Pain ◽  
Back Pain ◽  
Low Back

Chronic Low Back Pain Management in Primary Care

2003 ◽  
Vol 28 (8) ◽  
pp. 26-31 ◽  
Author(s):  
Kelly Phillips ◽  
Anne P.Y. Ch’ien ◽  
Barbara R. Norwood ◽  
Chris Smith
Keyword(s):  
Primary Care ◽  
Low Back Pain ◽  
Back Pain ◽  
Pain Management ◽  
Chronic Low Back Pain ◽  
Low Back ◽  
Back Pain Management

scholarly journals Prognostic ability of STarT Back Screening Tool combined with work-related factors in patients with low back pain in primary care: a prospective study

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046446
Author(s):  
Monica Unsgaard-Tøndel ◽  
Ottar Vasseljen ◽  
Tom Ivar Lund Nilsen ◽  
Gard Myhre ◽  
Hilde Stendal Robinson ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Primary Care ◽  
Low Back Pain ◽  
Back Pain ◽  
High Risk ◽  
Work Ability ◽  
High Risk Group ◽  
Low Back ◽  
Related Factors

ObjectivePrimary care screening tools for patients with low back pain may improve outcome by identifying modifiable obstacles for recovery. The STarT Back Screening Tool (SBST) consists of nine biological and psychological items, with less focus on work-related factors. We aimed at testing the prognostic ability of SBST and the effect of adding items for future and present work ability.MethodsProspective observational study in patients (n=158) attending primary care physical therapy for low back pain. The prognostic ability of SBST and the added prognostic value of two work items; expectation for future work ability and current work ability, were calculated for disability, pain and quality of life outcome at 3 months follow-up. The medium and high-risk group in the SBST were collapsed in the analyses due to few patients in the high-risk group. The prognostic ability was assessed using the explained variance (R2) of the outcomes from univariable and multivariable linear regression and beta values with 95% CIs were used to assess the prognostic value of individual items.ResultsThe SBST classified 107 (67.7%) patients as low risk and 51 (32.3%) patients as medium/high risk. SBST provided prognostic ability for disability (R2=0.35), pain (R2=0.25) and quality of life (R2=0.28). Expectation for return to work predicted outcome in univariable analyses but provided limited additional prognostic ability when added to the SBST. Present work ability provided additional prognostic ability for disability (β=−2.5; 95% CI=−3.6 to −1.4), pain (β=−0.2; 95% CI=−0.5 to −0.002) and quality of life (β=0.02; 95% CI=0.001 to 0.04) in the multivariable analyses. The explained variance (R2) when work ability was added to the SBST was 0.60, 0.49 and 0.47 for disability, pain and quality of life, respectively.ConclusionsAdding one work ability item to the SBST gives additional prognostic information across core outcomes.Clinical trial number:NCT03626389

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