Improved efficiency of patient admission with electronic health records in neurosurgery

Introduction

The quality of medical notes in healthcare can impact on patient safety and on quality of patient care (El-Kareh et al., 2009; Schiff and Bates, 2010), while gaps in these notes can lead to medical errors (Adane et al., 2019), inadequate patient care and poor quality indicators for specific treatment. Traditional paper-based medical documentation is gradually being replaced by electronic health records (EHRs), which appear to be a key component of modern and effective healthcare (Soto et al., 2002). A national survey of physicians in 2014 demonstrated that 75% of physicians who responded said they were using EHRs. Of these respondents, 65% also indicated an improvement in patient care, while only 5% reported negative effects on the quality of care (Collier, 2015). Another recent study showed that medical residents spent of average 5.4 hours per working day on EHR actions such as chart reviews, note entries or reviewing results (Wang et al., 2019). This highlights the importance of such notes, since they are the basis of medical decisions. On the one hand, EHRs are able to increase the readability and completeness of medical notes (Burke et al., 2015), but there are concerns that EHRs might also lead to more time-consuming documentation without any further quality improvements because the doctors in charge may require more time for typing text into computers than they would writing the same notes on paper (Walsh, 2004). This means that smart and efficient EHRs that support the care provider to input brief and accurate documentation are needed.

To facilitate an assessment of medical notes with standardised criteria and an option to benchmark the quality of documentations, QNOTE was developed through a multi-stakeholder process with an emphasis on clarity, completeness and organisation (Hanson et al., 2012), and validated in a population of ambulatory patients with type 2 diabetes (Burke et al., 2014). This tool allows an assessment of the existence of relevant data and how these data are presented by scoring 12 elements in handwritten and electronic clinical notes (chief complaints, history of present illness, problem list, past medication history, medication list, adverse drug reactions and allergies, social and family history, review of systems, physical findings, assessment, plan of care and follow-up information) (Burke et al., 2015). The advantage of QNOTE is that it allows an assessment that balances the existence of relevant data and how it is presented (Burke et al., 2014). Furthermore, it enables the differential assessment of free-text sections apart from overall notes. Many other assessment tools have focused primarily on the presence or absence of specific data (Callen et al., 2010; Soto et al., 2002; Stengel et al., 2004), or do not allow this differential assessment with global scoring method (e.g. PDQI-9) (Stetson et al., 2012).

In this study, we investigated whether the introduction of an electronic template-based admission form for the collection of information about the patient’s medical history and neurological and clinical state at admission in the neurosurgical unit might have an impact on the “quality” of documentation in a discharge record and the amount of time taken to produce this documentation.

Method

In the subject hospital, the main hospital information system (HIS) used in all departments except for intensive care units was Orbis (AGFA HealthCare GmbH, Bonn, Germany). This study was performed only in the neurosurgical department. To reduce training periods and ensure a proper interface between different forms, the new EHR was programmed into the HIS by a programmer for the use in the neurosurgical unit. After a stakeholder analysis with the whole team of neurosurgeons, where substantial key points were determined, a tree-like structured admission form was developed. It allowed free text acquisition for medical, social and family history, as well as diagnosis, but contained a complete neurological assessment based on point-and-click, template-driven boxes of predefined symptomatic conditions. This EHR was integrated into the existing workflow and allowed an automatic transfer of relevant information into the medical discharge reports and other electronic forms. The authorised neurosurgeons received two phases of training in the use of the form. In the first phase, training focused on the technical feasibility of the admission form. After 2 months, a second training phase focused on textual optimisation.

Admission forms in the neurosurgical unit are routinely and exclusively produced by the neurosurgeon in charge due to local regulations. No other care provider is permitted to produce or change this document. Thus, the admission form formed the basis of a large portion of the clinical information contained in the discharge records, dictated by a neurosurgeon and transcribed by an office assistant. This workflow process remained unchanged for handwritten and digital admission forms.

In August 2016, 30 handwritten admission forms were assessed by recording the time needed for documentation and dictation of the discharge records by the neurosurgeon in charge. Afterwards, the forms were assessed by scoring their quality with QNOTE (group 1). Thereby, the documentation was measured by the QNOTE instrument on a 100-point scale in 10-point steps, as suggested by Burke et al. (2014). The assessment was performed by two independent neurosurgeons who were not involved in the production of the admission forms. Six months after implementation of the new EHR, the procedure was repeated with a further 30 digital admission forms (group 2). Because of prior existing EHRs for medication and further therapeutic planning, these elements of QNOTE (elements past medication history, medication list, plan of care and follow-up information) were not assessed for this form. To ensure comparability of the notes and the dictation of medical discharge records, only medical notes and dictations from German native-speaking neurosurgeons and patients not treated in an intensive care unit were evaluated.

Results

The mean length of stay for groups 1 and 2 (10.2 days and 9.7 days, respectively) was not significantly different. Sixty-three per cent (group 1) and 60% (group 2) of the patients suffered from symptomatic brain tumours or metastases, 26.6% (group 1) and 33.3% (group 2) were inpatients because of degenerative spine diseases. Ten per cent (group 1) and 6.7% (group 2) were patients with hydrocephalus or chronic pain.

The mean documentation time for handwritten admission forms was 8.1 min ± 4.1 min and the dictation time for discharge records was 10.6 min ± 3.5 min (group 1). After implementation of EHRs, the documentation time increased slightly to 9.6 min ± 2.3 min but was not significantly different (group 2). Meanwhile, the time for dictation was reduced to 5.1 min ± 1.2 min (p < 0.0001) (Figure 1(a)). The QNOTE mean grand score for handwritten notes was 44 ± 30 and was significantly higher for EHRs with 87 ± 22 (p < 0.0001) (Figure 1(b)). Equally, all element scores were significantly higher for EHRs (see Table 1 for additional details). There was a significant strong correlation between higher mean grand scores and a higher documentation time with r = 0.623; p < 0.01 for handwritten notes and a moderate significant correlation (r = 0.49; p < 0.01) for EHRs. Shorter dictation times correlated strongly and significantly with higher mean grand scores in both groups (r = 0.792; p < 0.01 for handwritten notes and r = 0.563; p < 0.01 for EHR).

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Figure 1.

(a) Comparison of needed time for documentation of handwritten and digital admission forms and dictation of medical discharge records. (b) QNOTE Grand Score of handwritten and digital forms (higher value represents higher quality).

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Table 1.

QNOTE mean element scores ± SD for handwritten forms and EHR.^a

Elements	Handwritten form (group 1)	EHR (group 2)
Chief complaint	62 ± 28	87 ± 22
History of present illness	45 ± 29	86 ± 20
Problem list	40 ± 28	88 ± 23
Past medical history
Medications
Adverse drug reactions and allergies	46 ± 33	86 ± 23
Social and family history	41 ± 34	93 ± 18
Review of systems	51 ± 29	84 ± 23
Physical findings	40 ± 27	89 ± 20
Assessment	41 ± 26	83 ± 24
Plan of care
Follow-up information
Grand score	44 ± 33	87 ± 22

EHR: electronic health record.

^a All element scores for EHR were significantly increased (p < 0.0001) compared to scores for handwritten forms. Grey-shaded section denotes that elements not assessed because of prior existing EHR for medication and further therapeutic planning.

Discussion

In the present study, the implementation of EHRs improved the quality of patient admission documentation significantly and was able to reduce the aggregated time needed for documentation of admission forms and dictation of discharge records in a neurosurgical hospital department. Considering a workload of about 30 admissions per week in our clinic, this means a time saving of 2 hours per week for documentation.

The advantage of this digital admission form with point-and-click, template-driven checkboxes was that the neurosurgeon in charge was guided through the neurological examination with fewer gaps in the examination, and the inclusion of mandatory fields to ensure that important conditions were noted. Furthermore, participants reported that the ability to choose between checkboxes was time saving compared to writing the findings manually, which accords with other recent research findings (Chen et al., 2019). Additionally, with EHRs, possible illegible notes can be avoided (Embi et al., 2004), which contributes to a higher quality of documentation (Jamieson et al., 2017) while the instantaneous availability of medical information may reduce risks to patient care (Soto et al., 2002) and costs (Pare et al., 2014; Yoshida et al., 2013).

One potential shortcoming with such point-and-click boxes is that they might mislead physicians to click for symptomatic conditions on the basis of an uncritical assumption (Shoolin et al., 2013). Regarding this issue, it is not possible to verify medical findings without a second examination and the QNOTE score does not provide certainty as to whether the presented data are conclusive.

The major and most time-consuming aspect of the documentation process was to typewrite the free text of patient history. It could be assumed that the time needed could be further reduced by the possibility of dictating the text with automatic speech recognition during the admission process. Unfortunately, this technique was not available at the time of this study under local conditions. Although the accuracy rate of speech recognition ranges from 88.9% (Johnson et al., 2014) to 96% (Pezzullo et al., 2008), recent reports show that it is steadily maturing over time (Hodgson and Coiera, 2016). Thereby, the time needed for speech recording would seem to be comparable with the time needed for dictation and transcription (Mohr et al., 2003; Rana et al., 2005), but the document turnaround time could be decreased by 81% for speech recording (Krishnaraj et al., 2010).

The option to transfer the diagnosis, patient history and the neurological examination into the discharge report with only one click significantly reduced the dictation time. During the implementation period of the EHRs, the documentation time initially increased because the physicians had to familiarise themselves with the new form and the typewriting. Therefore, the neurosurgeons in charge wrote only short notes to reduce the initial increased documentation time or copied and pasted from other records as already shown by Thielke et al. (2007). Consequently, these free texts could not be transferred into discharge records without revision, which, in the end, led to no time-saving effects, but rather confusion and errors (Hirschtick, 2006). However, after a few weeks of participating in the changeover phase, with frustration increasing due to the additional time required for documentation (a normal human reaction during change management, according to Zell (2003)), with further training the participants adapted their phrasing into full sentences with no need for further corrections. This finally reduced the dictation time significantly, because the information in the admission form could be transferred into the discharge records without any correction or need for dictation. Thus, acceptance of EHRs for daily work during the measurement period was increased and could explain the correlation between the quality of documentation and dictation time. Based on these experiences, the use of additional digital forms may facilitate information regarding patients’ symptoms. Furthermore, those standardised and high-quality clinical data could be used for clinical research, as suggested by other researchers (Cowie et al., 2017), although this also raises concerns regarding privacy and safety issues (Ajami and Bagheri-Tadi, 2013; Brelsford et al., 2018).

Conclusion

Implementation of EHRs can improve the quality of patient admission documentation and reduce the time taken for dictation of discharge records in specific medical fields. However, in the implementation of EHRs, care is needed to ensure quality of documentation is preserved, and to be mindful of the potential impact of change and management issues on the process. It is crucial to involve stakeholders and users of the EHRs in a timely way during the development stage, and also important to monitor the testing and implementation phase carefully to ensure optimal results and better usability.