Glucose Monitors Upload Data Directly Into a the Ehr System. This Is an Example of
Abstruse
Telemedicine tin can facilitate population health management by extending the accomplish of providers to efficiently treat high-risk, high-utilization populations. However, for telemedicine to be maximally useful, data collected using telemedicine technologies must exist reliable and readily available to healthcare providers. To accost current gaps in integration of patient-generated health information into the electronic health record (EHR), nosotros examined ii patient-facing platforms, Epic MyChart and Apple HealthKit, both of which facilitated the uploading of blood glucose data into the EHR as role of a diabetes telemedicine intervention. All patients were offered utilize of the MyChart platform; nosotros subsequently invited a purposive sample of patients who used the MyChart platform finer (n = 5) to likewise use the Apple tree HealthKit platform. Patients reported both platforms helped with diabetes cocky-management, and providers appreciated the convenience of the processes for obtaining patient information. Providers stated that the EHR data presentation format for Apple HealthKit was challenging to interpret; however, they besides valued the greater perceived accurateness the Apple HealthKit data. Our findings signal that patient-facing platforms tin can feasibly facilitate manual of patient-generated health data into the EHR and back up telemedicine-based care.
The transition to value-based payment models emphasizes the need for effective approaches to population health. i–3 Telemedicine, a type of remote patient monitoring that uses technology to facilitate contact between patients and healthcare providers, 4 can expedite population wellness management by extending the ability of providers to come across the needs of high-run a risk, loftier-utilization populations in an efficient manner. 5 Considering the majority of patient self-intendance occurs outside of the provider's role, telemedicine enables providers to engage patients in their home setting with greater regularity. 4 , vi
Telemedicine may hold particular promise as a means to amend medical management of costly chronic illnesses similar diabetes. 7–nine Under telemedicine-based approaches, patient-facing technologies can be used to collect patient-generated wellness data (PGHD) and enable telemonitoring (monitoring of patients with diabetes in between provider visits). Using PGHD, telemedicine interventions that include telemonitoring, self-management support, and medication direction have been shown to exist more than effective than episodic in-person appointments for poorly controlled diabetes. 8 , ten–12
While existent-fourth dimension drove of PGHD can facilitate telemedicine-based disease direction, xiii PGHD are seldom incorporated into electronic wellness records (EHRs) where providers can hands and conveniently access these information. xiv , 15 Our current inability to integrate PGHD into EHRs deters widespread implementation of constructive telemedicine interventions. sixteen–18 This integration gap represents both a source of frustration for providers, who must interrupt their workflow to navigate unfamiliar consumer technologies to access PGHD, and a missed opportunity to maximize the benefits of telemedicine-based care. sixteen , 17
To address electric current barriers to telemedicine-based diabetes care, we developed and tested 2 processes for integrating PGHD collected from patient-facing technologies directly into the EHR. Nosotros discuss our processes for integrating PGHD into the EHR, patient perceptions of utilizing devices to collect and transfer health data into our wellness organization'south EHR, and provider perspectives of PGHD in the EHR.
METHODS
Nosotros conducted a half dozen-month pilot projection, Diabetes Direction – The Adjacent Generation (DM-TNG), examining the feasibility of delivering an effective telemedicine intervention to high-risk diabetes patients using novel processes for integrating PGHD into the EHR.
Sample and setting
Nosotros recruited patients with blazon 2 diabetes from 2 primary care clinics in a southeastern U.Southward. city. Inclusion criteria included glycated hemoglobin persistently ≥eight.v% for ≥1 yr (minimum of 2 measurements over the past year) despite engagement with dispensary-based intendance (defined as a minimum of two clinic appointments over the past year). We identified patients using the EHR and provider referrals; research staff contacted eligible patients by telephone to discuss the project and enrollment procedure. The project was conducted nether a Quality Improvement exemption from the local University Health Organization Institutional Review Board.
Telemedicine intervention design
The telemedicine intervention in this projection is effective for patients with persistently poor diabetes control 8 and includes 3 components: telemonitoring, cocky-management instruction, and physician-guided medication management. During the half dozen-month intervention period, telemedicine nurses delivered the telemonitoring and self-management support intervention components over 12 biweekly phone encounters, during which interim blood glucose data were reviewed, medications reconciled, and medication adherence discussed. During most telephone encounters, telemedicine nurses delivered educational modules salient to diabetes self-management. Following each telephone run across, the nurse compiled a summary written report in the EHR; this report was routed to the projection's medication provider for the medication management component of the intervention. Patients could contact the project team with concerns almost blood glucose values, blood glucose monitoring, or apply of these data collection platforms. Data flow is depicted in Figure 1.
Figure 1.
Overview of data flow and patient-provider interaction for Diabetes Management-The Next Generation (DM-TNG) intervention. EHR: electronic wellness record.
Figure 1.
Overview of data flow and patient-provider interaction for Diabetes Management-The Next Generation (DM-TNG) intervention. EHR: electronic health record.
Patient-facing technology platforms
As function of delivering the telemedicine intervention, we used 2 commercially-available patient-facing information technologies, Epic MyChart (Ballsy Systems, Verona, WI) and Apple HealthKit (Apple Inc, Cupertino, CA), to transfer patient-generated self-monitored blood glucose information directly into our health system'south Epic-based EHR. The MyChart platform is the commercial patient portal associated with Ballsy EHR and enables advice between patients and providers. In both processes described subsequently, the PGHD were uploaded automatically into the EHR and were and so displayed via flowsheets (ie, documents in the patient's chart displaying medical data). We lacked the resources to develop custom flowsheets for the purposes of this feasibility pilot, so we utilized flowsheets for both platforms that had been developed previously, and were already bachelor for employ by providers inside our healthcare organisation. We describe the context of use for each procedure in Table 1.
Table i.
Context of utilise for MyChart and Apple HealthKit
| MyChart | Apple tree HealthKit | |
|---|---|---|
| Steps undertaken to transfer PGHD from personal claret glucose meter |
|
|
| Frequency and timing of entering in data | Patients were encouraged to enter blood glucose values in MyChart daily | Patients were encouraged to connect to wireless cyberspace daily to enable the transfer of data into the EHR |
| Steps taken for providers to view PGHD |
|
|
| MyChart | Apple HealthKit | |
|---|---|---|
| Steps undertaken to transfer PGHD from personal blood glucose meter |
|
|
| Frequency and timing of inbound in information | Patients were encouraged to enter blood glucose values in MyChart daily | Patients were encouraged to connect to wireless internet daily to enable the transfer of data into the EHR |
| Steps taken for providers to view PGHD |
|
|
EHR: electronic wellness record; PGHD: patient-generated wellness data.
Table ane.
Context of utilise for MyChart and Apple HealthKit
| MyChart | Apple HealthKit | |
|---|---|---|
| Steps undertaken to transfer PGHD from personal claret glucose meter |
|
|
| Frequency and timing of entering in data | Patients were encouraged to enter blood glucose values in MyChart daily | Patients were encouraged to connect to wireless internet daily to enable the transfer of data into the EHR |
| Steps taken for providers to view PGHD |
|
|
| MyChart | Apple HealthKit | |
|---|---|---|
| Steps undertaken to transfer PGHD from personal claret glucose meter |
|
|
| Frequency and timing of inbound in information | Patients were encouraged to enter claret glucose values in MyChart daily | Patients were encouraged to connect to wireless internet daily to enable the transfer of data into the EHR |
| Steps taken for providers to view PGHD |
|
|
EHR: electronic health record; PGHD: patient-generated wellness data.
Process 1: Epic MyChart
All patients receiving the telemedicine intervention were offered use of the MyChart platform. Patients used their personal claret glucose monitors and manually entered their claret glucose information into the MyChart platform via home computers or smart device. Patients were encouraged to enter blood glucose data into MyChart daily. For EHR users to admission blood glucose information in MyChart, project staff used a flowsheet adapted by our squad that compiled relevant data from the patient'southward MyChart. When the flowsheet was ordered, information from the patient's MyChart were sent to the EHR. The study of PGHD in the EHR using MyChart is depicted in Figure two.
Figure two.
Flowsheet report for MyChart patients.
Figure 2.
Flowsheet report for MyChart patients.
Process 2: Apple HealthKit
In order to test the feasibility of the Apple HealthKit platform, we purposively selected patients (n = 5) who had completed the bulk of the scheduled biweekly telephone encounters and consistently entered blood glucose values into MyChart during the six-month intervention. These patients used Apple HealthKit for an additional iii months, during which they continued to receive the telemedicine intervention.
For patients participating in the Apple HealthKit phase of the project, we provided an Apple iPod Touch equipped with Apple HealthKit and a Food and Drug Administration–approved glucose meter by iHealth. Apple HealthKit is a platform that allows individuals to organize their wellness monitoring data on i dashboard that is hands accessible by the patient. Patients using the Apple tree HealthKit platform did not log in to MyChart to manually enter blood glucose data; instead, the iHealth glucose meter relayed blood glucose information to the iPod Touch via Bluetooth and Apple HealthKit and so automatically transmitted these data into the EHR whenever a wireless internet connexion was bachelor. The written report of PGHD in the EHR using Apple HealthKit is depicted in Figure 3.
Figure 3.
Flowsheet report for Apple HealthKit patients.
Figure three.
Flowsheet study for Apple HealthKit patients.
For EHR users to access blood glucose data, project staff used a flowsheet adapted by our squad that compiled relevant information from Apple HealthKit users. When the flowsheet was ordered, the iPod requested patient permission for the team to admission relevant information in Apple HealthKit. The patient had to accept this prompt before data were sent to the EHR. Once the patient gave permission, the EHR accessed but the requested data, and the patient could revoke EHR admission to these data at any time.
Evaluation measures
We invited patients to participate in semistructured interviews about their experiences participating in the project (Table two). A research assistant conducted all patient interviews via telephone. AAL conducted email interviews with the project's endocrinologists (NEJ, MJC) to obtain provider perceptions regarding use of PGHD in the EHR. We used directed content analysis 19 to compare statements nigh MyChart with statements well-nigh Apple HealthKit. AAL and CD independently coded and analyzed these qualitative information and then reviewed all findings with a 3rd investigator (MJC) until consensus was reached.
Table 2.
Patient and provider semistructured interview questions
| Patient-specific questions |
Questions specific to MyChart:
|
| Provider questions |
|
| Patient-specific questions |
Questions specific to MyChart:
|
| Provider questions |
|
a Questions only asked if patient used the Apple HealthKit technology in improver to MyChart.
EHR: electronic health record.
Table 2.
Patient and provider semistructured interview questions
| Patient-specific questions |
Questions specific to MyChart:
|
| Provider questions |
|
| Patient-specific questions |
Questions specific to MyChart:
|
| Provider questions |
|
a Questions merely asked if patient used the Apple HealthKit technology in addition to MyChart.
EHR: electronic wellness record.
RESULTS
Enquiry staff approached 86 patients; 35 declined to participate and 16 were excluded upon further screening (ie, no access to the Internet or smartphone). The remaining 35 patients who enrolled in the project were majority female and African American (Table iii). Research staff invited participants to complete semistructured interviews, and 10 patients agreed to participate. Of these 10, 6 used the MyChart platform and iv used both MyChart and Apple HealthKit. The provider sample included the 2 endocrinologist medication managers for the telemedicine intervention.
Tabular array iii.
Patient characteristics
| Total sample (N = 35) | Interview completers (n = ten) | |
|---|---|---|
| Female | 24 (68.vi) | four (forty) |
| Race/ethnicity | ||
| African American | 27 (77.1) | 9 (90) |
| White | seven (20) | 1 (10) |
| No Answer | 1 (2.9) | 0 (0) |
| Technology platform used | ||
| MyChart only | xxx (86) | 7 (70) |
| MyChart and Apple HealthKit | 5 (14) | 3 (30) |
| Calls completed | ||
| <3 | 15 (43) | 1 (10) |
| ≥iii | xx (57) | 9 (90) |
| Full sample (N = 35) | Interview completers (n = x) | |
|---|---|---|
| Female person | 24 (68.6) | 4 (xl) |
| Race/ethnicity | ||
| African American | 27 (77.1) | ix (90) |
| White | seven (20) | i (10) |
| No Answer | 1 (2.nine) | 0 (0) |
| Technology platform used | ||
| MyChart only | 30 (86) | 7 (lxx) |
| MyChart and Apple HealthKit | 5 (xiv) | 3 (thirty) |
| Calls completed | ||
| <3 | 15 (43) | i (10) |
| ≥3 | twenty (57) | 9 (90) |
Table 3.
Patient characteristics
| Full sample (North = 35) | Interview completers (n = 10) | |
|---|---|---|
| Female | 24 (68.6) | 4 (40) |
| Race/ethnicity | ||
| African American | 27 (77.1) | 9 (xc) |
| White | seven (twenty) | 1 (10) |
| No Respond | one (two.ix) | 0 (0) |
| Technology platform used | ||
| MyChart simply | xxx (86) | vii (70) |
| MyChart and Apple HealthKit | v (14) | 3 (xxx) |
| Calls completed | ||
| <3 | 15 (43) | 1 (x) |
| ≥3 | xx (57) | nine (90) |
| Total sample (N = 35) | Interview completers (n = x) | |
|---|---|---|
| Female | 24 (68.6) | iv (xl) |
| Race/ethnicity | ||
| African American | 27 (77.1) | 9 (90) |
| White | 7 (twenty) | 1 (10) |
| No Answer | 1 (2.9) | 0 (0) |
| Technology platform used | ||
| MyChart only | 30 (86) | 7 (lxx) |
| MyChart and Apple HealthKit | 5 (14) | 3 (30) |
| Calls completed | ||
| <3 | 15 (43) | one (ten) |
| ≥three | 20 (57) | 9 (90) |
Patient perspectives
Most patients (n=vii of 10) stated that using MyChart or Apple tree HealthKit aided their diabetes self-management. I patient stated, "I love the charting…I loved looking at information technology daily or weekly, just seeing the trends," and another stated, "[entering blood sugar values into MyChart] affected [me] because I knew what was going on. It helped me to remember [self-management]." However, every bit the patients using the MyChart platform had to manually enter information, remembering to enter in these information sometimes proved challenging. I MyChart user stated, "Just trying to remember to do it [was challenging]…by hand yous could only enter one solar day at a time. It'southward not like information technology was an open up spreadsheet where I could easily just put them all in without having to click 5 different times to get to some other day." Patients who used Apple HealthKit found the automated transfer of data from the glucose meter to the EHR less cumbersome. One patient stated, "[Apple HealthKit] made it easier – during the first phase [of the project] where I had to enter everything into the reckoner [using MyChart], it was done weekly, but [with Apple HealthKit] information technology was done daily and it made information technology easier for me." 1 MyChart patient who did not use Apple tree HealthKit commented that automatic uploading of data would accept been helpful. "[I would like a] glucose monitor that actually, when you stick yourself, automatically sends that calculator reading to MyChart, so [the blood glucose values] wouldn't actually accept to be entered in [manually]."
Several patients experienced technological challenges using MyChart or Apple HealthKit, including logging into MyChart, manually entering data into MyChart, using the iPod, and the battery life of the iPod. Only ane patient indicated that the technical challenges completely impeded her ability to upload blood glucose data using MyChart or Apple tree HealthKit.
Provider perspectives
The 2 project clinicians indicated that integration of PGHD into the EHR facilitated telemedicine-based medication direction for diabetes, because information were readily available for review. The providers could easily access the EHR flowsheet without having to be familiar with multiple information-sharing platforms. One provider stated, "Compared to what I would take to do to go equivalent data otherwise, such every bit calling the patient and transcribing the numbers, or logging into a split up website, having these data come straight into the EHR saved multiple steps." Providers stated that automatic uploading of information with Apple HealthKit from the iHealth meter increased PGHD accuracy, because the blood sugar values were not self-reported, and were therefore less susceptible to fault or falsification. However, while the PGHD entered via MyChart were systematically organized by mealtime, data entered via Apple HealthKit were displayed in a unmarried long row. Considering the MyChart display facilitated interpretation of blood glucose trends, both clinicians preferred the MyChart display over Apple tree HealthKit (Figure 3).
DISCUSSION
Telemedicine can facilitate improved population wellness direction, 3 , 5 but depends on reliable and accurate PGHD. To address current gaps in EHR integration of patient-collected data, we examined 2 patient-facing platforms designed to facilitate the uploading of PGHD into the EHR. We establish that (i) despite certain challenges, use of the platforms assisted diabetes self-direction and (2) PGHD can be feasibly integrated into a health arrangement'southward EHR.
Mobile health technologies that facilitate the collection of PGHD are ane way to further population health management efforts for patients with complex chronic illnesses. v The capability to monitor health information between office visits, especially in challenging patient populations, could improve patient outcomes and reduce healthcare costs. PGHD help provide a more complete flick of the patient's self-management behaviors and allow clinicians to make patient-centered medical decisions.
While accurate PGHD are essential for informing clinical decision making, commonly bachelor PGHD collection methods are oft suboptimal. Approaches that rely on patient self-report—such as the MyChart platform used for this projection or manual logging of blood glucose data in a hardcopy logbook—place substantial responsibility on patients to actively document data. Unless a patient takes the necessary steps to report their data (and in the example of hardcopy logbooks, remembers to bring data to dispensary appointments), the PGHD remain unavailable to providers. Additionally, self-reported PGHD are subjective and may not accurately reverberate the patient'due south truthful blood glucose values. Our findings suggest that using processes like the Apple tree HealthKit platform to integrate PGHD into the EHR, without relying on self-report, may have particular potential to improve patient and provider experiences.
Our findings also indicate that the presentation format for PGHD in the EHR is of import to consider. Our 2 PGHD-collection platform flowsheets resulted in distinct data display formats in the EHR due to differences in the flowsheets' programming. Information from MyChart were organized in columns by mealtime, while data obtained via Apple HealthKit were presented in a single row. Our providers appreciated how each platform facilitated the availability of PGHD in the EHR, but strongly preferred the MyChart format. Overall, our results betoken that the optimal mechanism for incorporating PGHD into the EHR should include (1) passive/automatic transfer of data from the patient's monitoring device into the EHR, which assures data availability and accurateness, and (2) a convenient format for data presentation in the EHR, which facilitates interpreting and acting upon data. In diabetes, these changes would facilitate easy and authentic interpretation of blood glucose information in real time.
Limitations
Certain factors limit the generalizability of our findings. Because we interviewed simply x patients and 2 providers, the reported perceptions of MyChart and Apple HealthKit may not stand for the wider patient and provider populations. Additionally, due to the scope of the project, we could select simply v patients to examine the feasibility of the Apple HealthKit platform. Finally, because the focus of this project was to explore the feasibility of EHR integration of PGHD, we did not verify the accuracy of these data transferred into the EHR. Despite these limitations, our findings provide insight into the value and challenges of EHR integration of PGHD.
CONCLUSION
This project represents an important step toward implementing telemedicine-based intendance delivery models in clinical practice. Our findings indicate that EHR integration of PGHD is feasible, and may back up telemedicine-based diabetes care. Future enquiry should focus on strategies to heighten the usability of platforms for integrating PGHD into EHRs and appropriate back up for providers utilizing PGHD in patient direction.
FUNDING
This piece of work was supported past a Duke Institute for Health Innovation Grant; Department of Veterans Affairs Office of Academic Affiliations Grant No. TPH 21-000 (to AAL); Durham Center of Innovation to Accelerate Discovery and Practice Transformation Grant No. CIN 13-410 (to AAL); Department of Veterans Affairs Health Services Research and Development Service Career Evolution Honor Grant No. CDA 13-261 (to MJC); and Department of Veterans Affairs Health Services Enquiry and Development Service Research Career Scientist Award Grant No. 08-027 (to HBB). The content is solely the responsibleness of the authors and does not necessarily reflect the position or policy of Duke University, the U.Due south. Department of Veterans Affairs, or the U.Due south. government.
AUTHOR CONTRIBUTIONS
AAL analyzed qualitative data and wrote the manuscript. CD assisted with qualitative data analysis and manuscript editing. RJS, GLJ, and HBB assisted with data estimation and manuscript editing. MO and SG assisted with conduct of project, collected data, and assisted with manuscript editing. NEJ served as a clinical provider during projection and assisted with manuscript editing. MJC obtained project funding, led conduct of project, analyzed and interpreted data, and helped write manuscript.
Conflict of interest statement
HBB reports receiving inquiry funds from Sanofi, Otsuka, Johnson and Johnson, and Improved Patient Outcomes every bit well as consulting funds from Sanofi, Otsuka, and Abbott. The remaining authors accept no competing interests to declare.
REFERENCES
i
Burwell
SM.
Setting value-based payment goals—HHS efforts to improve U.S. health care
.
N Engl J Med
2015
;
372
x
:
897
–
nine
.
2
Jacobson
RM
, Isham GJ Finney Rutten LJ.
Population health as a means for wellness intendance organizations to evangelize value
.
Mayo Clin Proc
2015
;
90
xi
:
1465
–
seventy
.
3
Sharma
A
, Harrington RA McClellan MB
Using digital health technology to better generate evidence and deliver prove-based care
.
J Am Coll Cardiol
2018
;
71
23
:
2680
–
90
.
5
Kvedar
J
, Coye MJ Everett W.
Connected health: a review of technologies and strategies to ameliorate patient care with telemedicine and telehealth
.
Wellness Aff (Millwood)
2014
;
33
2
:
194
–
nine
.
6
Faruque
LI
, Wiebe N Ehteshami-Afshar A
Effect of telemedicine on glycated hemoglobin in diabetes: a systematic review and meta-analysis of randomized trials
.
CMAJ
2017
;
189
9
:
E341
–
64
.
7
Lee
SWH
, Chan CKY Chua SS Chaiyakunapruk N.
Comparative effectiveness of telemedicine strategies on blazon 2 diabetes management: a systematic review and network meta-analysis
.
Sci Rep
2017
;
7
1
:
12680.
eight
Crowley
MJ
, Edelman D McAndrew AT
Practical telemedicine for veterans with persistently poor diabetes control: a randomized pilot trial
.
Telemed J E Health
2016
;
22
v
:
376
–
84
.
9
Flodgren
G
, Rachas A Farmer AJ Inzitari Thou Shepperd S.
Interactive telemedicine: effects on professional practice and health intendance outcomes
.
Cochrane Database Syst Rev
2015
;
9
:
CD002098
.
10
Klonoff
DC.
Using telemedicine to meliorate outcomes in diabetes–an emerging engineering science
.
J Diabetes Sci Technol
2009
;
iii
4
:
624
–
8
.
xi
Wakefield
BJ
, Koopman RJ Keplinger LE
Upshot of abode telemonitoring on glycemic and blood pressure command in primary intendance dispensary patients with diabetes
.
Telemed J E Health
2014
;
twenty
iii
:
199
–
205
.
12
Wakefield
BJ
, Holman JE Ray A
Effectiveness of home telehealth in comorbid diabetes and hypertension: a randomized, controlled trial
.
Telemed J E Health
2011
;
17
four
:
254
–
61
.
13
Heintzman
ND.
A digital ecosystem of diabetes information and applied science: services, systems, and tools enabled by wearables, sensors, and apps
.
J Diabetes Sci Technol
2016
;
ten
1
:
35
–
41
.
14
Kumar
RB
, Goren ND Stark DE Wall DP Longhurst CA.
Automated integration of continuous glucose monitor information in the electronic health tape using consumer technology
.
J Am Med Inform Assoc
2016
;
23
iii
:
532
–
seven
.
xv
North
F
, Chaudhry R.
Apple healthkit and health app: patient uptake and barriers in master care
.
Telemed J E Wellness
2016
;
22
7
:
608
–
13
.
16
Cohen
DJ
, Keller SR Hayes GR Dorr DA Ash JS Sittig DF.
Integrating patient-generated wellness data into clinical care settings or clinical decision-making: lessons learned from project healthdesign
.
JMIR Hum Factors
2016
;
iii
two
:
e26.
17
Reading
MJ
, Merrill JA.
Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review
.
J Am Med Inform Assoc
2018
;
25
6
:
759
–
71
.
18
Lv
N
, Xiao L Simmons ML Rosas LG Chan A Entwistle M.
Personalized hypertension management using patient-generated health information integrated with electronic health records (EMPOWER-H): six-month pre-post written report
.
J Med Internet Res
2017
;
19
ix
:
e311.
19
Hsieh
H-F
, Shannon SE.
Three approaches to qualitative content analysis
.
Qual Health Res
2005
;
15
9
:
1277
–
88
.
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