Hearing and Balance Screening and Referrals for Medicare Patients: A National Survey of Primary Care Physicians

Johnson, Carole E.; Danhauer, Jeffrey L.; Koch, Lindsey L.; Celani, Kristina E.; Lopez, Ilian Priscilla; Williams, Victoria

doi:10.3766/jaaa.19.2.7

Cited by 42 publications

(73 citation statements)

References 37 publications

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“…161,162,165,167,179,181,188,207,216,242,250,270,271,277 Of these, three studies reported that a GP who had been in practice longer referred less frequently. 162,165,207 Conversely, three studies reported that GPs who had been in practice longer referred more frequently.…”

Section: General Practitioner Factorsmentioning

confidence: 99%

“…242 An additional seven studies reported no association between number of years in practice and referral rate. 161,179,181,227,250,270,271 Overall, three of these studies 162,227,271 were at higher risk of bias and the evidence was graded as conflicting.…”

Section: General Practitioner Factorsmentioning

confidence: 99%

“…161,162,164,167,169,171,176,179,181,183,193,215,216,227,[270][271][272][273][274][275][276] Again, the picture was very mixed, with four studies reporting higher rates of referral for younger GPs 193,215,272,274 and six studies reporting higher rates of referral for older GPs. 162,169,171,216,273,275 Thirteen studies reported no association.…”

Section: General Practitioner Factorsmentioning

confidence: 99%

“…162,169,171,216,273,275 Thirteen studies reported no association. 161,164,167,176,178,179,181,183,227,270,271,272,276 Three of these studies 162,227,271 were at higher risk of bias and the evidence was graded as conflicting.…”

Section: General Practitioner Factorsmentioning

confidence: 99%

“…161,164,167,[178][179][180][181]183,207,216,225,227,248,[270][271][272][273]275,276,[279][280][281][282][283] Eleven studies suggested that females refer more frequently, 181,207,216,225,272,273,275,[279][280][281][282] with one further study 248 discussing perceived male barriers to referral. However, 12 studies reported no association between sex and referral outcomes.…”

Section: General Practitioner Factorsmentioning

confidence: 99%

See 4 more Smart Citations

What is the evidence on interventions to manage referral from primary to specialist non-emergency care? A systematic review and logic model synthesis

Blank

Baxter

Woods

et al. 2015

Health Services and Delivery Research

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BackgroundDemand management describes any method used to monitor, direct or regulate patient referrals. Several strategies have been developed to manage the referral of patients to secondary care, with interventions targeting primary care, specialist services, or infrastructure.ObjectiveThis research aimed to conduct an inclusive systematic review and logic model synthesis in order to better understand factors impacting on the effectiveness of interventions targeting referral between primary and secondary medical health care.DesignThe approach combined systematic review with logic modelling synthesis techniques to develop an evidence-based framework of factors influencing the pathway between interventions and system-wide changes.SettingPrimary health care.Main outcome measuresReferral from primary to secondary care.Review methodsSystematic searches were undertaken to identify recent, relevant studies. Quality of individual studies was appraised, with consideration of overall strength of evidence. A narrative synthesis and logic model summary of the data was completed.ResultsFrom a database of 8327 unique papers, 290 were included in the review. The intervention studies were grouped into four categories of education interventions (n = 50); process change interventions (n = 49); system change interventions (n = 38); and patient-focused interventions (n = 3). Effectiveness was assessed variously in these papers; however, there was a gap regarding the mechanisms whereby these interventions lead to demand management impacts. The findings suggest that, although individual-level interventions may be popular, the stronger evidence relates only to peer-review and feedback interventions. Process change interventions appeared to be more effective when the change resulted in the specialist being provided with more or better quality information about the patient. System changes including the community provision of specialist services by general practitioners, outreach provision by specialists and the return of inappropriate referrals appeared to have evidence of effect. The pathway whereby interventions might lead to service-wide impact was complex, with multiple factors potentially acting as barriers or facilitators to the change process. Factors related, first, to the doctor (including knowledge, attitudes and beliefs, and previous experiences of a service), second, to the patient (including condition and social factors) and, third, to the influence of the doctor–patient relationship. We also identified a number of potentially influential factors at a local level, such as perceived waiting times and the availability of a specialist. These elements are key factors in the pathway between an intervention and intended demand management outcomes influencing both applicability and effectiveness.ConclusionsThe findings highlight the complexity of the referral process and multiple elements that will impact on intervention outcomes and applicability to a local area. Any interventions seeking to change referral practice need to address factors relating to the individual practitioner, the patient and also the situation in which the referral is taking place. These conclusions apply especially to referral management in a UK context where this whole range of factors/issues lies well within the remit of the NHS. This work highlights that intermediate outcomes are important in the referral pathway. It is recommended that researchers include measure of these intermediate outcomes in their evaluation of intervention effectiveness in order to determine where blocks to or facilitators of system-wide impact may be occurring.Study registrationThe study is registered as PROSPERO CRD42013004037.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

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Section: General Practitioner Factorsmentioning

confidence: 99%

Section: General Practitioner Factorsmentioning

confidence: 99%

Section: General Practitioner Factorsmentioning

confidence: 99%

Section: General Practitioner Factorsmentioning

confidence: 99%

Section: General Practitioner Factorsmentioning

confidence: 99%

See 3 more Smart Citations

What is the evidence on interventions to manage referral from primary to specialist non-emergency care? A systematic review and logic model synthesis

Blank

Baxter

Woods

et al. 2015

Health Services and Delivery Research

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Screening for Hearing Loss in Older Adults

Force¹,

Krist

Davidson

et al. 2021

JAMA

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IMPORTANCE Age-related sensorineural hearing loss is a common health problem among adults. Nearly 16% of US adults 18 years or older report difficulty hearing. The prevalence of perceived hearing loss increases with age. Hearing loss can adversely affect an individual's quality of life and ability to function independently and has been associated with increased risk of falls, hospitalizations, social isolation, and cognitive decline. OBJECTIVE To update its 2012 recommendation, the US Preventive Services Task Force (USPSTF) commissioned a systematic review on screening for hearing loss in adults 50 years or older. POPULATION Asymptomatic adults 50 years or older with age-related hearing loss. EVIDENCE ASSESSMENT Because of a lack of evidence, the USPSTF concludes that the benefits and harms of screening for hearing loss in asymptomatic older adults are uncertain and that the balance of benefits and harms cannot be determined. More research is needed. RECOMMENDATION The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening for hearing loss in older adults.

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Machine Learning Prediction of Objective Hearing Loss With Demographics, Clinical Factors, and Subjective Hearing Status

Gathman

Choi

Vasdev

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveHearing loss (HL) is highly prevalent, yet underrecognized and underdiagnosed. Lack of standardized screening, awareness, cost, and access to hearing testing present barriers to HL identification. To facilitate prescreening and selection of patients who warrant audiometric evaluation, we developed a machine learning (ML) model to predict speech‐frequency pure‐tone average (PTA).Study DesignCross‐sectional study.SettingNational Health and Nutrition Examination Survey (NHANES).MethodsThe cohort included 8918 adults (≥20 years) who completed audiometric testing with NHANES (2012‐2018). The primary outcome measure was the prediction of better hearing ear speech‐frequency PTA. Relevant predictors included demographics, medical conditions, and subjective assessment of hearing. Supervised ML with a tree‐based architecture was used. Regression performance was determined by the mean absolute error (MAE) with binary classification assessed with area under the receiver operating characteristic curve (AUC).ResultsUsing the full set of predictors, the test set MAE between the ML‐predicted and actual PTA was 5.29 dB HL (95% confidence interval [CI]: 4.97‐5.61). The 5 most influential predictors of higher PTA were increased age, worse subjective hearing, male gender, increased body mass index, and history of smoking. The 5‐factor abbreviated model performed comparably to the extended feature set with MAE 5.36 (95% CI: 5.03‐5.69) and AUC for PTA > 25 dB HL of 0.92 (95% CI: 0.90‐0.94).ConclusionThe ML model was able to predict PTA with patient demographics, clinical factors, and subjective hearing status. ML‐based prediction may be used to identify individuals who could benefit most from audiometric evaluation.

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Hearing and Balance Screening and Referrals for Medicare Patients: A National Survey of Primary Care Physicians

Cited by 42 publications

References 37 publications

What is the evidence on interventions to manage referral from primary to specialist non-emergency care? A systematic review and logic model synthesis

What is the evidence on interventions to manage referral from primary to specialist non-emergency care? A systematic review and logic model synthesis

Screening for Hearing Loss in Older Adults

Machine Learning Prediction of Objective Hearing Loss With Demographics, Clinical Factors, and Subjective Hearing Status

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