Early Changes in Billing and Notes After Evaluation and Management Guideline Change.

BACKGROUND: The American Medical Association updated guidance in 2021 for frequently used billing codes for outpatient evaluation and management (E/M) visits. The intent was to account for provider time outside of face-to-face encounters and to reduce onerous documentation requirements. OBJECTIVE: To analyze E/M visit use, documentation length, and time spent in the electronic health record (EHR) before and after the guideline change. DESIGN: Observational, retrospective, pre-post study. SETTING: U.S.-based ambulatory practices using the Epic Systems EHR. PARTICIPANTS: 303 547 advanced practice providers and physicians across 389 organizations. MEASUREMENTS: Data from September 2020 through April 2021 containing weekly provider-level E/M code and EHR use metadata were extracted from the Epic Signal database. We descriptively analyzed overall and specialty-specific changes in E/M visit use, note length, and time spent in the EHR before and after the new guidelines using provider-level paired t tests. RESULTS: Following the new guidelines, level 3 visits decreased by 2.41 percentage points (95% CI, -2.48 to -2.34 percentage points) to 38.5% of all E/M visits, a 5.9% relative decrease from fall 2020. Level 4 visits increased by 0.89 percentage points (CI, 0.82 to 0.96 percentage points) to 40.9% of E/M visits, a 2.2% relative increase. Level 5 visits (the highest acuity level) increased by 1.85 percentage points (CI, 1.81 to 1.89 percentage points) to 10.1% of E/M visits, a 22.6% relative increase. These changes varied by specialty. We found no meaningful changes in measures of note length or time spent in the EHR. LIMITATION: The Epic ambulatory client base may underrepresent smaller and independent practices. CONCLUSION: Immediate changes in E/M coding contrast with null findings for changes in both note length and EHR time. Provider organizations are positioned to respond more rapidly to billing process changes than to changes in care delivery and associated EHR use behaviors. Fully realizing the intended benefits of this guideline change will require more time, facilitation, and scaling of best practices that more directly address EHR documentation practices and associated burden. PRIMARY FUNDING SOURCE: None.

I had not time to make it shorter: an exploratory analysis of how physicians reduce note length and time in notes.

OBJECTIVE: We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout. MATERIALS AND METHODS: We used EHR metadata from January to May, 2021 for 130 079 ambulatory physician Epic users. We identified cohorts of physicians who decreased note length and/or documentation time and analyzed changes in their note composition. RESULTS: 37 857 physicians decreased either note length (n = 15 647), time in notes (n = 15 417), or both (n = 6793). Note length decreases were primarily attributable to reductions in copy/paste text (average relative change of -18.9%) and templated text (-17.2%). Note time decreases were primarily attributable to reductions in manual text (-27.3%) and increases in note content from other care team members (+21.1%). DISCUSSION: Organizations must consider priorities and tradeoffs in the distinct approaches needed to address different contributors to EHR burden. CONCLUSION: Future research should explore scalable burden-reduction initiatives responsive to both note bloat and documentation time.

Patient-reported exposures and outcomes link the gut-brain axis and inflammatory pathways to specific symptoms of severe mental illness.

We developed a "gut-brain-axis questionnaire" (GBAQ) to obtain standardized person-specific "review of systems" data for microbiome-gut-brain-axis studies. Individual items were compared to PANSS symptom measures using dimensional, transdiagnostic and traditional categorical approaches. METHOD: Forty psychotic participants, independent of diagnoses, and 42 without psychosis (18 nonpsychotic affective disorders, 24 healthy controls) completed the GBAQ and underwent research diagnostic and symptom assessments. The PANSS scales and its dysphoric mood, autistic preoccupation and activation factors were computed. RESULTS: Transdiagnostic analyses robustly linked psychosis severity to constipation (p<.001), and Negative (p=.045) and General Psychopathology scores (p=.016) with bowel hypomotility. Activation factor scores predicted numbers of psychiatric (p=.009) and medical conditions (p=.003), BMI (p=.003), skin (p<.001) and other conditions. Categorical analyses comparing psychotic, nonpsychotic and control groups revealed behavioral differences: cigarette smoking (p=.013), alcohol use (p=.007), diet (p's <.05), exercise (p<.001). All subjects accurately self-reported their diagnosis. CONCLUSIONS: The GBAQ is a promising tool. Transdiagnostic analyses associated psychotic symptoms to gut hypomotility, indicative of low gut vagal tone, consistent with reduced cardiovagal activity in psychosis. Activation, similar to delirium symptoms, predicted medical comorbidity and systemic inflammatory conditions. Group level comparisons only showed behavioral differences. Underpinnings of psychiatric disorders may include reduced gut vagal function, producing psychosis, and systemic inflammation, impacting risks for psychotic and nonpsychotic conditions.

An integrative study of the microbiome gut-brain-axis and hippocampal inflammation in psychosis: Persistent effects from mode of birth.

The mechanism producing psychosis appears to include hippocampal inflammation, which could be associated with the microbiome-gut-brain-axis (MGBS). To test this hypothesis we are conducting a multidisciplinary study, herein described. The procedures are illustrated with testing of a single subject and group level information on the impact of C-section birth are presented. METHOD: Study subjects undergo research diagnostic interviews and symptom assessments to be categorized into one of 3 study groups: psychosis, nonpsychotic affective disorder or healthy control. Hippocampal volume and metabolite concentrations are assessed using 3-dimensional, multi-voxel H1 Magnetic Resonance Imaging (MRSI) encompassing all gray matter in the entire hippocampal volume. Rich self-report information is obtained with the PROMIS interview, which was developed by the NIH Commons for research in chronic conditions. Early trauma is assessed and cognition is quantitated using the MATRICS. The method also includes the most comprehensive autonomic nervous system (ANS) battery used to date in psychiatric research. Stool and oral samples are obtained for microbiome assessments and cytokines and other substances are measured in blood samples. RESULTS: Group level preliminary data shows that C-section birth is associated with higher concentrations of GLX, a glutamate related hippocampal neurotransmitter in psychotic cases, worse symptoms in affective disorder cases and smaller hippocampal volume in controls. CONCLUSION: Mode of birth appears to have persistent influences through adulthood. The methodology described for this study will define pathways through which the MGBA may influence the risk for psychiatric disorders.

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, "at-risk" phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.