Categorizing Patient-Provider Secure Communications to Measure Substitutionary Effect on In-person Services

Transcription

1 Categorizing Patient-Provider Secure Communications to Measure Substitutionary Effect on In-person Services Ted E. Palen, PhD, MD, MSPH Institute for Health Research Colorado Permanente Medical Group Kaiser Permanente Colorado, Denver Colorado Di Meng, PhD Director of Analytics and Evaluation Health Information Technology Transformation and Analytics Kaiser Permanente, Oakland California Terhilda Garrido VP, Health Information Technology Transformation and Analytics Kaiser Permanente, Oakland California

2 Patient Portals and Online Health Access: Redefining Access Empowering Patients

3 Transforming Primary Care Encounters Care delivery has expanded beyond office visits. It is now also done via the phone and secure s. In 2003, there were essentially 0% secure s Now SEs represent 35% of the primary care patient encounters. There were a total of 23 million secure s in % KP Program Wide 80% 49% 60% Office Visits Video Visits* * 6295 in TPMG Jan 2014-Sept 2014 Secure 40% 20% 35% Telephone Office Visits 0% % Source: UCDA Core Value Metrics

4 KP Mobile 2013 kp.org / mobile 84M visits to kp.org 15M secure s 24M lab views 8M refills 4.4M patients online! 27% of transactions mobile

5 Trends by encounter type Many factors impact KP s office visit rate: aging, product mix, benefit structure Since the broader implementation of MyChart with secure s, KP overall Primary care OV rate has decreased slightly. - All Regions PCP Encounter rates PM Trends by encounter type Office Visits Secure s PCP 08Q1-14Q4 Calls Total encounters + 19% Office Visits - 17% Secure % Phone +14% All Departments 08Q1 14Q Office Visits - 6% Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 10Q4 11Q1 11Q2 11Q3 11Q4 12Q1 12Q2 12Q3 12Q4 13Q1 13Q2 13Q3 13Q4 14Q1 14Q2 14Q3 14Q4

6 Background: Previous Studies Understanding association of secure on utilization. NW 2005 Zhou et al: Still very early on in our experience, Zhou found a 7-10% decrease in PC office visits and 17% fewer PC phone calls. CO 2012 Palen et al: CO found a significant increase (7%-38%) in office visits, phone calls, urgent care visits, ED visits and hospitalizations. Other studies; Mixed results

7 Study 1: Zhou et al. Pre period Determine baseline util Background Year / region: Index date Kp.org registration Post period Utilization impact? In 2007 a study of NW experience (then the most advanced region with the PHR) was done The kp.org registration rate was 6% among adult members 1 Impact Impact: 7-10% PC office visits, 17% PC phone calls Method: Retrospective, pre-post cohort design Matched cohort design, matching on Age, gender, chronic condition, PCP, baseline office visit utilization Office Visits Phone Calls 9.7% 6.7% 16.7% Pre Post Difference Study Group Matched Matched Control Study Group Group Matched Control Group Matched Study Group 1 Zhou, Yi Yvonne; Garrido, Terhilda; Chin, Homer; Wiesenthal, Andrew; Liang, Louise (2007); Patient access to an electronic health record with secure messaging: impact on primary care utilization, The American Journal of Managed Care, Vol 13: , July 2007

8 Study 2: Palen et al Pre period Determine baseline util Index date Kp.org registration Post period Utilization impact? Background Year / region: Work published by CO in 2012 Impact Impact: 26% office visits 5% phone calls 22% urgent care 8% ED visits 38% hospitalizations Method: Retrospective, pre-post cohort design Matched cohort design, propensity score matching: Age, gender, race/ethnicity, # of chronic conditions, baseline office visit utilization The kp.org registration rate was approaching 60% among adult members 1 (26%) (5%) (8%) (22%) (38%) 1 Palen Ted E, Ross Colleen, Powers J David, Xu Stanley. Association of Online Patient Access to Clinicians and Medical Records With Use of Clinical Services. JAMA. 2012;308(19):

9 Current Study: Design and Methods Retrospective data from Kaiser Permanente Colorado (KPCO) Retrospective, pre-post cohort design Matched cohort design, propensity score matching: Age, gender, race/ethnicity, # of chronic conditions, baseline office visit utilization, patient calls, urgent care, ED, inpatient services, risk score Members aged 18 and over Continuously enrolled for at least 30 months in Identified which s contained diagnosis code(s) and or order code(s) Categorized s containing diagnosis code(s) in to low acuity conditions based on diagnosis related group categories Determined if the patient had a subsequent in-person appointment for this DRG

10 The Difference Design Methods Can Make In both the Zhou and Palen studies, a spike in utilization near the index date (kp.org registration /use) was noted. For the Zhou study, in 2005 with 5% kp.org registration rates, the spike was considered an artifact. Going in to the clinic for a visit, a patient was exposed to banners and staff encouragement to register. The spike was moderate and short-lived. In 2011, CO adult kp.org registration rates were nearing 60%. To find patients with 2.5 years of no kp.org use and then abruptly choosing to register/use kp.org was more challenging and potentially biasing. One hypothesis is that this patient population had very recently acquired a new, serious health condition thereby catalyzing high utilization of all KP care services which included kp.org. Original method: - Match on pre baseline utilization - Remove 1-4 months of spike / index utilization Meng / Palen revised method: - Match on pre baseline utilization - Match on index date spike utilization Office Visit rates Index date Office Visit Rates Matching Period (1-9) Matching Matching Period (1-12) Period (1-12) Evaluation Period (19-30) Index date When Patient Registered and Activated KP.org Account Month Index date When Patient Initiated Secure Month *10,584 matched pairs included, 11,737 before matching, 90% matching rate *9,345 matched pairs included, 11,737 before matching, 80% matching rate 10

11 Study 3 Pre period Determine baseline utilization Background Index date First patient Initiated Secure date Post period Utilization impact? Year / region: Kaiser Permanent Colorado, The kp.org registration rate is approaching 63% among adult members Impact Impact: The third study found no statistically significant difference in office visits, inpatient services, calls or urgent care visits between the study and control groups. Method: Retrospective, pre-post cohort design Matched cohort design, propensity score matching: Age, gender, race/ethnicity, # of chronic conditions, baseline office visit utilization, patient calls, urgent care, ED, inpatient services, Risk Score, etc. (-4%

12 VirtuWell Low Acuity Condition Evaluation Design Low Acuity Conditions Using low acuity conditions we evaluated follow up utilization. Low Acuity Conditions: acne bladder infection breast infection burn common cold chlamydia diagnosis chlamydia screen cough ear pain flu diagnosis gonorrhea lice nail pink eye skin rash stye sunburn throat trichomoniasis yeast infection SEs with a Diagnosis Top3 Secure Low Acuity Condition: Cough (20%) Acne (18%) UTI Related ( 10%) *based on CO 2013 FY data 12

13 SE Diagnosis-coded Substitution Only a portion of SEs have been coded by clinicians. These tend to be SEs with labs or med orders. For these SEs, there is no follow up visit or phone call 85% of the time. Given the clinical nature (as evidenced by a dx code), care service provided (as evidenced by orders placed), and the lack of follow up, these may SEs substitute for a visit (as evidenced by the purple section below). Secure s coded with a diagnosis have strong substitutive effect of office visits (87% of these s had procedures or medication orders ) Secure For SEs with a dx, there is no office visit or telephone follow up encounter 85% of the time. Secure s contained a coded order 21% of the time and 85% of these members did not have a follow-up office visit or telephone call in the next 30 days SE followed by other utilization *based on CO 2013 FY data

14 Low Acuity Conditions Follow up Utilization? Only 8% of SEs for low acuity conditions resulted in a follow up visit. This is comparable to office visits for low acuity conditions (5% follow up). Please note that there may be an opportunity to shift 48% of the visits where a low acuity condition was the primary reason for the office visit to a SE or a phone visit. Low Acuity Condition as Primary Dx - By Modality- Follow Up - By Modality - Opportunity? *based on CO 2013 FY data 14

15 Summary The effect of secure s on utilization is complex. The most productive studies of SEs may be for a subpopulation or specific use case / episode. For patients with high and acute needs, SEs may simply provide additional care, improved direction on the next clinical step and/or access. In some situations, system-generated secure s assure office visits / kept appointments. For low acuity conditions, there is a strong substitutive effect. And in fact, there may be the potential for more virtual care. For chronic conditions, we see the glimmer of visit reduction (this will be studied further) For SEs with a coded diagnosis (usually with a med or lab order), there is substitution at a rate that is consistent with what we hear from patients. Perhaps 20% of the patient-initiated SEs may substitute for office visits Further work is needed to better understand these complex dynamics. Next Steps: The study is refining its next phase of work to focus on 1. Topics / work that could be triaged away from the MD 2. Topics / areas of focus that could lead to direct substitution of face-to-face encounters e.g. post knee surgery follow-up questionnaires.