Characteristics associated with involuntary versus voluntary legal status at admission and discharge among psychiatric inpatients

Background The objective of this analysis was to determine the ways in which patients’ legal statuses at hospital admission and discharge are associated with select sociodemographic and clinical variables. This study specifically investigated differences between patients who were voluntary during both admission and discharge, patients who were involuntary on admission but voluntary on discharge (having converted to voluntary status during hospitalization), and patients who were involuntary during both admission and discharge.
Introduction
In the United States, criteria for involuntary psychiatric hospitalization generally require imminent dangerousness to oneself or others, or complete inability to care for oneself [1, 2], though specific criteria vary between states [3]. An important rationale for involuntary psychiatric hospitalization is that patients admitted for evaluation often have severe deficits in their capacities to make treatment decisions [4, 5]. Patients often revise their beliefs about the need for treatment during or after hospitalization. A study conducted by Gardner et al. [6] found that slightly more than half of patients who initially stated (upon admission) that they did not need hospitalization later admitted they indeed had needed treatment when re-interviewed several weeks after discharge.
It has been suggested that involuntary treatment may reduce the likelihood that patients will voluntarily seek care in the future [7, 8]. Subsequent aversion to psychiatric services may be due to a distrust of clinicians arising from coercive tactics or a lack of procedural justice (e.g., the patient is treated unfairly, disrespected, ignored, or excluded from the decision-making process) encountered during hospitalization [1, 8, 9]. On the other hand, involuntary hospitalization may provide patients with needed treatment, even if involuntarily, that results in improved outcomes and treatment adherence [10]. Some research indicates that the presence or absence of coercion is accurately reflected by the patient’s legal status, but other studies demonstrate that coercive pressures and perceived coercion do not necessarily correlate well with legal status in all settings [8, 11].
Evidence associating legal status with sociodemographic and clinical variables is sparse and inconsistent. One study found that higher re-admission rates and more unstable discharge living arrangements were associated with involuntary legal status on hospital admission [12]. A meta-analytic review conducted using studies from the 1950s through the early 1980s found that the only significant outcome measure correlated with admission legal status was length of stay [13]. Studies have reached opposing conclusions regarding whether or not male gender is significantly associated with involuntary legal status, especially when controlling for severity of illness [7, 12, 14]. With regard to clinical characteristics, psychotic disorder diagnoses, greater illness severity, and more frequent preadmission assaultive behaviors may be associated with involuntary admission [13]. Even fewer studies have examined factors associated with converting from involuntary to voluntary legal status during hospitalization [14–16]. Cuffel [14] identified greater clinical improvement, less severe diagnoses, and non-minority ethnicity as factors predictive of converting to voluntary status early in the hospitalization. Later during hospitalization, the only factor associated with legal status conversion was having discharge living arrangements involving family and friends.
Studies that find significant associations between legal status and outcomes may influence policies relating to resource allocation during hospitalization, discharge planning, and outpatient follow-up [12, 15]. For instance, research characterizing those at risk for very short involuntary hospitalization (i.e., discharge upon expiration of short-term commitment) addresses the need for more thorough and engaged evaluation and treatment while hospitalized. One may be able to enhance treatment adherence with a more focused treatment and evaluation plan, in conjunction with closer follow-up after discharge [12]. Though legal status is often overlooked in studies in which it would be particularly relevant, it is clearly an important variable for further research.
The purpose of the present study was to compare three groups of hospitalized psychiatric patients: (1) patients admitted and discharged voluntarily (VOL-VOL), (2) patients admitted involuntarily who later signed-in voluntarily and were discharged voluntarily (INV-VOL), and (3) patients admitted and discharged on an involuntary legal status (INV-INV). As such, this trichotomous criterion variable addressed the change (or lack thereof) in patients’ legal status during hospitalization, allowing an examination of whether or not certain sociodemographic and clinical factors are associated with differences in admission-to-discharge legal status. Treating legal status as a trichotomous variable (rather than solely considering either admission or discharge legal status) may yield meaningful results that add to the current body of research on correlates of patients’ legal status.
Methods
Subjects and setting
This analysis was part of a study aimed at determining predictors of non-adherence with the first scheduled community mental health outpatient appointment following hospitalization [17]. Inclusion in the study was restricted to consecutively discharged patients with a scheduled follow-up appointment at any of 12 participating community mental health settings in a single urban county. Patients re-hospitalized during the course of data collection were not included a second time, in order to maintain independence of observations. This analysis included data that were collected on 227 patients from December 2003 to July 2004.
All patients were admitted to the 22-bed inpatient unit or the 8-bed crisis stabilization unit of a large, public-sector hospital in the southeastern United States. Both the longer-stay inpatient unit and the crisis stabilization unit admit patients primarily for evaluation and treatment of first episodes of illness or exacerbations of severe and persistent mental illnesses (primarily schizophrenia and other psychotic disorders and severe affective disorders). This county hospital serves a predominantly low-income, urban, African American population.
In the state of Georgia, commitment for involuntary evaluation can be initiated by a licensed physician, psychologist, clinical social worker, or psychiatric clinical nurse specialist. Taking custody for transportation to an emergency evaluation facility also can be carried out by a law enforcement officer. In either case, this initial commitment allows for the individual to be taken to and held at a locked evaluation facility. Commitment criteria include the fact that the person appears to have a mental illness, and either (1) presents a substantial risk of imminent harm to self or others as manifested by recent overt acts or threats of violence presenting a probability of physical injury, or (2) is so unable to care for his/her own physical health and safety as to create an imminently life-endangering crisis [3]. Within 48 h of admission, if the individual continues to meet commitment criteria, a physician may sign for commitment for further evaluation and treatment of up to five business days in duration. During this period of time, some patients will sign-in as voluntary patients. If the patient does not sign-in voluntarily, then he or she is typically discharged after the 5 days, unless commitment criteria clearly continue to be present. In that case, a court hearing is scheduled to determine whether or not the legal standard is met for longer-term involuntary hospitalization. However, in this setting extended commitment is used relatively rarely, and only in circumstances in which the threat to self or others is very apparent.
Procedures
The majority of data collected were abstracted from patients’ medical charts. Subjectively rated variables (e.g., treatment plan adherence during hospitalization, the treatment team’s opinion of likelihood of follow-up) were obtained by consulting with the social worker, the psychiatry resident or psychology intern, and the attending psychiatrist caring for the patient during hospitalization. As described in two recently published reports, basic demographic, socioeconomic, psychosocial, and clinical data were recorded in a systematic fashion using a standardized data collection instrument [17, 18]. The criterion variable of interest in this analysis was a three-level variable representing the patient’s legal status at admission and at discharge: VOL-VOL, INV-VOL, and INV-INV. The research was approved by the university’s institutional review board and the hospital’s research oversight committee.
Analysis
Bivariate analyses were performed using a variety of independent variables in relation to the criterion variable. Continuous variables were analyzed using one-way analysis of variance and categorical variables were analyzed using chi-square tests of association. These bivariate analyses were conducted to inform the selection of variables for inclusion in the subsequent multivariate regression analysis.
Polytomous logistic regression modeling was required due to the trichotomous nature of the admission/discharge legal status criterion variable. Those patients admitted and discharged voluntarily (VOL-VOL) were designated as the referent group in the regression model. Entering variables into the logistic model was based on significant bivariate test results (P < 0.05). A thorough assessment of confounding was conducted, and relevant confounding variables were controlled for in the model. The backward elimination method was used to identify variables that were independently significantly associated (P < 0.05) with legal status. Appropriate modeling diagnostics were conducted to ensure the fit of the model to the data. Adjusted odds ratios and 95% confidence intervals were computed to determine effect estimates for independent variables that were significantly associated with legal status, controlling for all other covariates in the model. The SAS version 9.1 software (SAS Inc., Cary, North Carolina, USA) was used for all statistical analyses.
Results
Basic sociodemographic and clinical descriptive statistics for the overall sample are shown in Table 1. Slightly over half (55.5%) of patients were female. The racial composition of the sample is consistent with that of the population served by the two inpatient units, and by the hospital in general. Specifically, 84.9% of patients were Black/African American. At the time of admission, 94.2% of patients were unemployed, and 25.6% reported being homeless. The majority of patients were single/never married (67.8%). The majority of patients had been previously hospitalized for psychiatric treatment (75.1%). An even greater percentage of patients were reported as having psychotic symptoms at the time of admission (85.8%). The mean age was 38.7 ± 12.4 years, and the mean educational level was 12.1 ± 2.3 years. The mean Global Assessment of Functioning (GAF) scale score on admission was 30.3 ± 8.2, ranging from 10 to 55.
Table 1Basic sociodemographic and clinical descriptive statistics for the overall sample (n = 227)VariableFrequencyGender, female126 (55.5%)Race    Black/African American191 (84.9%)    Hispanic/Latino4 (1.8%)    White/Caucasian/European American 28 (12.4%)    Other2 (0.9%)Employment status, unemployed213 (94.2%)Homeless on admission 58 (25.6%)Marital status    Single/never married154 (67.8%)    Married/living as married 19 (8.4%)    Separated/divorced/widowed 54 (23.8%)Prior psychiatric hospitalization169 (75.1%)Psychotic symptoms present on admission194 (85.8%)VariableMean ± SDMedianRangeAge 38.7 ± 12.44017–76Years of education 12.1 ± 2.312 4–18GAF score on admission 30.3 ± 8.23010–55
Among the 227 patients, 56 (24.7%) were in the VOL-VOL group, 81 (35.7%) were in the INV-VOL group, and 90 (39.6%) were in the INV-INV group. As shown in Table 2, select demographic and social variables were examined across the three groups. The difference in percentage of patients documented as having Axis IV psychosocial problems in the area of housing was statistically significant (χ2 = 6.43, df = 2, P = 0.04), with patients admitted and discharged voluntarily most likely to have housing problems (60.7%). The percentage of patients who were receiving disability payments—Supplemental Security Income (SSI) or Social Security Disability Insurance (SSDI)—over the past 3 months also differed significantly across the three legal status groups (χ2 = 6.82, df = 2, P = 0.03), with patients admitted and discharged involuntarily most likely to be receiving disability payments (61.1%).
Table 2Sociodemographic characteristics of 227 hospitalized patientsVariableLegal status on admission and dischargeaTest statistic (df)P-valueVOL-VOL (n = 56)INV-VOL (n = 81)INV-INV (n = 90)Gender, female32 (57.1%)48 (59.3%)46 (51.1%)1.23 (2)0.54Race, Black/African American45 (81.8%)66 (81.5%)80 (89.9%)2.87 (2)0.24Employed part-time or full-time2 (3.64%)7 (8.64%)4 (4.44%)1.99 (2)0.37Homeless on admission17 (30.4%)23 (28.4%)18 (20.0%)2.48 (2)0.29Current marital    Single/never married32 (57.1%)56 (69.1%)66 (73.3%)4.32 (4)0.36    Married/living as married6 (10.7%)7 (8.6%)6 (6.7%)    Separated/divorced/widowed18 (32.1%)18 (22.2%)18 (20.0%)Age on admission (Mean ± SD)41.0 ± 11.537.0 ± 13.238.8 ± 12.11.73 (222)0.18Axis IV housing problems34 (60.7%)35 (43.2%)36 (40.0%)6.43 (2)0.04Axis IV economic problems32 (57.1%)51 (63.0%)41 (45.6%)5.40 (2)0.07Receiving disability payments over past 3 months (SSI or SSDI)22 (39.3%)40 (49.4%)55 (61.1%)6.82 (2)0.03aLegal status on admission and discharge: VOL-VOL, Patients having voluntary legal status on both admission and discharge; INV-VOL, Patients having involuntary status on admission and voluntary status on discharge; INV-INV, Patients having involuntary legal status on both admission and discharge
Table 3 shows select clinical and diagnostic characteristics. The three legal status groups differed significantly on 13 of these 20 variables. The INV-INV group was most likely to have had previous psychiatric hospitalizations (84.3%), followed by 71.6% among the INV-VOL group, and 65.5% among the VOL-VOL group (χ2 = 7.27, df = 2, P = 0.03). Compared to the VOL-VOL group, patients who were admitted and discharged involuntarily were more likely to have a primary diagnosis of a psychotic disorder, to have a change in GAF score from admission to discharge of <20 points, and to have psychotic symptoms on discharge. Patients admitted and discharged involuntarily were less likely to have documentation of medical problems requiring medications at discharge, to be taking >2 psychiatric medications at discharge, and to have good treatment plan adherence during hospitalization compared to the VOL-VOL group. Not unexpectedly, length of stay in days (F = 20.45, df = 224, P < 0.0001) and the inpatient unit to which patients were admitted (χ2 = 32.84, df = 2, P < 0.0001) were highly significantly different across the groups.
Table 3Clinical and diagnostic characteristics of 227 hospitalized patientsVariableLegal status on admission and dischargeaTest statistic (df)P-valueVOL-VOL (n = 56)INV-VOL (n = 81)INV-INV (n = 90)Prior psychiatric hospitalization36 (65.5%)58 (71.6%)75 (84.3%)7.27 (2)0.03Psychotic symptoms on admission43 (76.8%)71 (87.7%)80 (89.9%)5.20 (2)0.07Primary diagnosis, schizophrenia or other psychotic disorder26 (47.3%)56 (69.1%)63 (70.0%)9.03 (2)0.01Comorbid substance use disorder diagnosis26 (46.4%)34 (42.5%)40 (44.4%)0.21 (2)0.90Comorbid personality disorder diagnosis10 (17.9%)13 (16.1%)12 (13.3%)0.58 (2)0.75Change of <20 in GAF score (Discharge–Admission)23 (42.6%)26 (32.9%)47 (53.4%)7.14 (2)0.03Depressive symptoms at discharge23 (41.1%)23 (28.4%)22 (24.4%)4.69 (2)0.10Anxious symptoms at discharge23 (41.1%)43 (51.1%)28 (31.1%)8.49 (2)0.01Psychotic symptoms at discharge27 (48.2%)51 (63.0%)67 (74.4%)10.34 (2)<0.01Documented medical problems requiring medication at discharge35 (62.5%)41 (50.6%)32 (35.6%)10.52 (2)<0.01>2 Psychiatric medications at discharge20 (36.4%)13 (16.1%)14 (15.9%)10.40 (2)<0.01Experiencing side effects at discharge7 (12.5%)23 (28.4%)13 (14.4%)7.41 (2)0.02Required seclusion5 (8.9%)11 (13.6%)18 (20.0%)3.52 (2)0.17Required restraints4 (7.1%)8 (9.9%)8 (8.9%)0.31 (2)0.86Required PRN medicine14 (25.0%)30 (37.0%)38 (42.2%)4.48 (2)0.11Patient has an established outpatient clinician15 (26.8%)20 (25.0%)38 (42.2%)6.78 (2)0.03Good treatment plan adherence during week prior to discharge41 (73.2%)60 (74.1%)50 (55.6%)8.06 (2)0.02Treatment team’s opinion of likelihood of follow-up    Poor15 (26.8%)16 (20.0%)35 (38.9%)15.02 (4)<0.01    Fair19 (34.0%)29 (36.3%)38 (42.2%)    Good22 (39.3%)35 (43.8%)17 (18.9%)Length of stay in days (Mean ± SD)11.3 ± 7.515.5 ± 8.48.4 ± 6.020.45 (224)<0.0001Unit, longer-stay inpatient unit36 (64.3%)67 (83.8%)37 (41.1%)32.84 (2)<0.0001a Legal status on admission and discharge: VOL-VOL, Patients having voluntary legal status on both admission and discharge; INV-VOL, Patients having involuntary status on admission and voluntary status on discharge; INV-INV, Patients having involuntary legal status on both admission and discharge
The average lengths of hospital stay (in days) across the three groups of patients are displayed in Fig. 1. Patients in the INV-INV group had an average length of hospitalization of 8.4 days, whereas patients in the VOL-VOL group had an average length of stay of 11.3 days. Patients in the INV-VOL group had the longest average length of hospital stay, at 15.5 days. Based on post-hoc tests using the Tukey approach, the difference between mean lengths of stay was significant for the INV-VOL group compared to the VOL-VOL group and for the INV-VOL group compared to the INV-INV group.
Fig. 1Average lengths of hospital stay (days) in the three groups of patients
Figure 2 illustrates the differences among the three groups in terms of the percentage of patients adhering with the first scheduled community mental health appointment following hospitalization. The average time interval between hospital discharge and the first scheduled community mental health appointment was 14 days and ranged from 0 (i.e., the appointment was scheduled to occur on same day as discharge) to 59 days. (Follow-up was assessed by a single phone call to the community mental health center where the discharged patient had been scheduled for the first outpatient appointment. The follow-up phone call was made a few days after the appointment, and further outpatient adherence was not tracked [17].) Patients in the INV-VOL group were the most likely to adhere with the first follow-up appointment (52.0%), whereas patients in the VOL-VOL group (30.9%) and the INV-INV group (27.1%) were far less likely to adhere (χ2 = 11.75, df = 2, P < 0.01).
Fig. 2Percentage of patients adhering with the first scheduled community mental health appointment following hospitalization
Based on the significant bivariate test results and a thorough consideration of relevant confounding variables, 18 factors were entered into the polytomous logistic regression model. In this model, three of these factors were independently associated with legal status: psychotic symptoms at discharge, documented medical problems requiring medication, and number of psychiatric medications. Psychiatric inpatient unit, length of hospital stay, prior psychiatric hospitalization, and receiving disability payments (SSI/SSDI) were deemed confounding variables and were, therefore, controlled for in the final model. Because of sparse missing values, modeling results were generated using data from 221 individuals. Table 4 shows model coefficients, standard errors, adjusted odds ratios, and 95% confidence intervals for each of the three significant variables, by legal status category.
Table 4Independent variables significantly associated with admission and discharge legal status among 221 hospitalized patientsVariableLegal status categoryaBSEaOR95% CIPsychotic symptoms at dischargeINV-VOL−0.020.440.980.42, 2.32INV-INV1.490.464.421.80, 10.86Documented medical problems requiring medication at dischargeINV-VOL−0.480.400.620.28, 1.35INV-INV−1.510.430.220.10, 0.51Number of psychiatric medications at discharge (>2)INV-VOL−1.800.520.170.06, 0.46INV-INV−0.880.510.410.15, 1.12The model controls for psychiatric hospital unit, length of hospital stay, prior psychiatric hospitalization, and receiving disability payments (SSI/SSDI)a VOL-VOL (voluntary legal status on both admission and discharge) is the referent group for all comparisons; INV-VOL, involuntary legal status on admission, voluntary status on discharge; INV-INV, involuntary legal status on both admission and dischargeB, Coefficient value from the final regression model; SE, standard error; aOR, adjusted odds ratio; CI, confidence interval
Patients who were involuntary on admission and discharge (INV-INV) were approximately 4.4 times more likely to have psychotic symptoms at discharge, relative to the VOL-VOL group. Patients in the INV-INV group were also about 4.5 times less likely (or equivalently, 0.22 times as likely) to have documented medical problems requiring medications at discharge, relative to those in the VOL-VOL group. Patients who were involuntary on admission and voluntary on discharge (INV-VOL) were approximately 6 times less likely (0.17 times as likely) to be prescribed more than two psychiatric medications at discharge.
Discussion
Multivariate logistic regression modeling yielded three clinical variables that were independently significantly associated with admission/discharge legal status, even after controlling for psychiatric unit, length of hospital stay, prior psychiatric hospitalization, and whether or not the patient received disability payments. As described below, the associations between legal status and each of these three significant variables potentially may be explained by the lower overall treatment engagement common among involuntary patients.
First, patients in the INV-INV group were much more likely to be experiencing psychotic symptoms at discharge, compared to the VOL-VOL group. The most plausible explanation for this finding relates to a difference in the level of acceptance of treatment between the two groups. Involuntary legal status is generally an indicator of a lack of treatment engagement, resistance to treatment, and/or impaired insight. Bivariate test results for “treatment plan adherence during the week prior to discharge” support the idea that involuntary patients generally had poorer overall treatment adherence.
When patients refuse to sign-in voluntarily and no longer meet dangerousness criteria, they may have to leave the hospital before resolution of psychotic symptoms. The abbreviated hospitalization may not have allowed for sufficient time for stabilization and adjustments to psychiatric medications. Due to the laws governing involuntary inpatient commitment in this state, individuals who have been hospitalized for the duration of the short-term commitment and who have refused to convert to voluntary status, must be discharged unless they clearly meet further commitment criteria [3]. On the other hand, patients with voluntary legal status are generally discharged based on clinical, rather than legal, criteria—they are released from the hospital when symptoms have sufficiently resolved and the patient is stable enough to return to the community.
A significant difference in psychotic symptoms at discharge was not found between the INV-VOL group and the VOL-VOL group. This is not surprising given the fact that these two groups were virtually equally as likely to be rated as having good treatment adherence during the week prior to discharge. Furthermore, these patients were discharged after sufficient resolution of symptoms rather than legal expiration. From a clinical vantage point, it appears that INV-INV patients are being discharged “too soon” due to legal constraints.
An alternate explanation for the difference in psychotic symptoms at discharge among the three groups is that INV-INV patients were more likely to have psychotic symptoms at the time of admission compared to the other two groups. However, the bivariate test revealed no statistically significant difference between the three groups in terms of psychotic symptoms present on admission.
Second, patients admitted and discharged involuntarily (INV-INV) were much less likely to have documented medical problems requiring medications at the time of discharge, relative to the VOL-VOL group. Because length of stay was controlled for in the model, the most likely reason for the difference in medical problems at discharge is a generalized lack of treatment engagement among involuntary patients. That is, poor treatment engagement is associated with a decreased likelihood of medical problems being reported and diagnosed. Involuntary patients are more likely to refuse medical history taking, physical examination, vital sign checks, diagnostic blood work, and other diagnostic procedures during hospitalization. Thus, clinicians may not have sufficient opportunities or information to detect common medical problems, such as hypertension, diabetes mellitus, and anemia.
It is notable that a significant difference in documented medical problems requiring medication was not found between the INV-VOL and VOL-VOL groups. As noted earlier, these two groups had similar treatment adherence during the week prior to discharge. These results support the idea that converting to voluntary status is associated with better treatment engagement, affording more opportunities for medical problems to be detected and medication to be prescribed. Future research is needed to determine whether a general lack of treatment engagement alone, or some additional factors in combination with poor treatment engagement, accounts for the difference in documented medical problems requiring medication.
Some alternate explanations as to why the INV-INV patients had fewer medical problems requiring medication at discharge merit consideration. For example, it is conceivable that even when clinicians detect a medical problem, they may be biased toward not prescribing medication to involuntary patients who are highly unlikely to agree to take it. However, in this particular study setting, when medical problems are detected in involuntary patients, clinicians typically prescribe the indicated medication, regardless of the likelihood that the patient will comply with taking it. Another possible explanation is that patients in the INV-INV group are generally healthier than the other two groups. However, because the three legal status groups were very similar with respect to age, gender, and race, it is highly unlikely that one group of patients would have been healthier than the other two.
Third, though the INV-VOL and VOL-VOL groups did not differ with respect to psychotic symptoms and medical problems requiring medication at discharge, these two groups did differ in the number of psychiatric medications at time of discharge. Those patients in the INV-VOL group were much less likely to have been prescribed more than two psychiatric medications at discharge, relative to the VOL-VOL group. The difference in number of psychiatric medications between the INV-INV and VOL-VOL groups was not significant, though the effect for the INV-INV group was in the same direction as with the INV-VOL group. The most plausible explanation for this finding may be related to both a lower overall treatment engagement among involuntary patients and differences in clinician prescribing behavior for involuntarily admitted patients compared to voluntary patients. That is, perhaps clinicians err on the side of caution with patients recently converting to voluntary status by limiting the number of psychiatric medications used to treat symptoms. Clinicians may limit the number of prescribed medications, especially if psychiatric symptoms are resolving, in order to simplify the medication regimen and sustain good treatment adherence. Interestingly, a much higher percentage of patients in the INV-VOL group adhered with the first community mental health appointment compared to the VOL-VOL group and the INV-INV group. It is conceivable that a simpler treatment regimen (one or two psychiatric medications) may contribute to higher rates of adherence with initial community mental health follow-up.
Several methodological limitations of this study merit consideration. First, the fact that patients are admitted voluntarily or involuntarily is a legal distinction, which may not accurately reflect the patient’s level of treatment engagement [11, 12, 19, 20]. Patients admitted voluntarily do not necessarily always have the motivation and adherence behaviors of individuals truly pursuing treatment for mental illness of their own accord [11–13]. For example, a voluntary patient who is pressured or coerced by family members to seek inpatient treatment may have motivation, adherence, and insight more similar to patients admitted involuntarily than to those voluntarily seeking treatment [11, 12]. Likewise, some involuntary patients may be more similar to voluntary patients than to other involuntary patients. This might occur when patients who would willingly have signed-in voluntarily are admitted involuntarily in order to overcome obstacles associated with voluntary admission procedures in certain mental health systems [11]. Because legal status may not be an appropriate surrogate for measuring perceived coercion or treatment engagement [5, 8, 11], future research should consider utilizing MacArthur’s Perceived Coercion Scale [21] and a continuous scale to assess treatment engagement.
A second methodological limitation is that commitment statutes “censor” the number of patients that convert to voluntary status, since non-converters must be discharged upon expiration of the commitment. Therefore, the true difference between the INV-VOL and INV-INV groups is somewhat obscured because of the limited timeframe in which patients have to convert to voluntary status. One cannot assume that any given INV-INV patient would never have converted to voluntary status; rather one can only infer that he or she had not done so by time of discharge. In other words, caution must be exercised in assigning ontological status to the INV-INV group. A third limitation is that patients re-admitted during the data collection period were excluded from the analysis. Future research might consider focusing on INV-INV patients with multiple hospitalizations. Another limitation with regard to data collection was that clinical and diagnostic variables (some of which were subjectively rated) were based on clinicians’ reports or information in the hospital chart, without using rating scales administered directly to patients. However, given the goals of this study, the researchers deemed patient assessments, beyond that done as part of routine evaluation and treatment, unnecessary. Also, involuntary patients would have been less likely to participate in formal research evaluations compared to voluntary patients, thus restricting the ability of the analyses to examine the issues of interest. Lastly, the findings from this study may have limited generalizability, given the specific sociodemographic and diagnostic characteristics of the sample. However, the population of interest to the researchers was that of minority individuals with severe psychiatric illnesses being treated in a large, urban, public-sector hospital.
Despite the methodological limitations inherent in the study design, there were also several strengths in the methodology and data analysis process. First, an extremely thorough assessment of confounding was conducted. The final model controlled for four variables that were considered likely confounders. Second, alternate modeling techniques were conducted, all of which resulted in the same findings. Lastly, a concerted effort was made to minimize missing values during data collection, and to prevent biases, the few variables with significant missing values were excluded from the analysis.
Conclusions
Research on associations between patients’ legal status and sociodemographic and clinical variables can directly and indirectly affect psychiatric treatment during and after hospitalization. For instance, patients in the INV-INV group have the shortest average length of hospitalization, which is a direct result of the commitment law dictating that patients cannot be involuntarily hospitalized for more than 5 days unless they continue to meet dangerousness criteria [3], even if symptoms have not yet resolved. In addition to being more likely to have psychotic symptoms at discharge, patients in the INV-INV group are less likely to have documented medical problems requiring medication at discharge. Thus, involuntary patients frequently may be discharged from the hospital with unresolved psychotic symptoms and undetected, untreated medical problems. The poor outpatient treatment follow-up rate among INV-INV patients suggests that this is a particularly vulnerable group needing focused attention by the mental health system.
During hospitalization, clinicians should be attentive to the manner in which involuntary patients are treated because this can greatly influence patients’ perceptions of coercion. It has been suggested that a concerted effort to reduce the level of coercive practices used during the admission process could mitigate some of the negative effects of coercion, such as future non-adherence with treatment and outpatient follow-up [8]. After discharge from the hospital, involuntary outpatient commitment can be employed to stabilize patients in the community, though this form of coercive treatment is also controversial. Recent research has suggested efficacy of involuntary outpatient commitment in enhancing treatment compliance and reducing re-admission rates among persons with severe psychiatric illnesses [22, 23]. Increased resource allocation for involuntary patients during the admission process and throughout hospitalization, discharge planning, and outpatient follow-up may have the downstream effect of preventing future hospital admissions and worsening of medical comorbidity.

involuntary voluntary commitment compulsory


Performance of a Web-Based Clinical Diagnosis Support System for Internists

BACKGROUND Clinical decision support systems can improve medical diagnosis and reduce diagnostic errors. Older systems, however, were cumbersome to use and had limited success in identifying the correct diagnosis in complicated cases.
INTRODUCTION
The best clinicians excel in their ability to discern the correct diagnosis in perplexing cases. This skill requires an extensive knowledge base, keen interviewing and examination skills, and the ability to synthesize coherently all of the available information. Unfortunately, the level of expertise varies among clinicians, and even the most expert can sometimes fail. There is also a growing appreciation that diagnostic errors can be made just as easily in simple cases as in the most complex. Given this dilemma and the fact that diagnostic error rates are not trivial, clinicians are well-advised to explore tools that can help them establish correct diagnoses.
Clinical diagnosis support systems (CDSS) can direct physicians to the correct diagnosis and have the potential to reduce the rate of diagnostic errors in medicine.1,2 The first-generation computer-based products (e.g., QMR—First Databank, Inc, CA; Iliad—University of Utah; DXplain—Massachusetts General Hospital, Boston, MA) used precompiled knowledge bases of syndromes and diseases with their characteristic symptoms, signs, and laboratory findings. The user would enter findings from their own patients selected from a menu of choices, and the programs would use Bayesian logic or pattern-matching algorithms to suggest diagnostic possibilities. Typically, the suggestions were judged to be helpful in clinical settings, even when used by expert clinicians.3 These diagnosis support systems were also useful in teaching clinical reasoning.4,5 Surprisingly and despite their demonstrated utility in experimental settings, none of these earlier systems gained widespread acceptance for clinical use, apparently related to the considerable time needed to input clinical data and their somewhat limited sensitivity and specificity.3,6 A study of Iliad and QMR in an emergency department setting, for example, found that the final impression of the attending physician was found among the suggested diagnoses only 72% and 52% of the time, respectively, and data input required 20 to 40 minutes for each case.7
In this study, we evaluated the clinical performance of “Isabel” (Isabel Healthcare Inc, USA), a new, second generation, web-based CDSS that accepts either key findings or whole-text entry and uses a novel search strategy to identify candidate diagnoses from the clinical findings. The clinician first enters the key findings from the case using free-text entry (see Fig 1). There is no limit on the number of terms entered, although excellent results are typically obtained with entering just a few key findings. The program includes a thesaurus that facilitates recognition of terms. The program then uses natural language processing and search algorithms to compare these terms to those used in a selected reference library. For Internal Medicine cases, the library includes 6 key textbooks anchored around the Oxford Textbooks of Medicine, 4th Edition (2003) and the Oxford Textbook of Geriatric Medicine and 46 major journals in general and subspecialty medicine and toxicology. The search domain and results are filtered to take into account the patient’s age, sex, geographic location, pregnancy status, and other clinical parameters that are either preselected by the clinician or automatically entered if the system is integrated with the clinician’s electronic medical record. The system then displays a total of 30 suggested diagnoses, with 10 diagnoses presented on each web page (see Fig. 2). The order of listing reflects an indication of the matching between the findings selected and the reference materials searched but is not meant to suggest a ranked order of clinical probabilities. As in the first generation systems, more detailed information on each diagnosis can be obtained by links to authoritative texts.
Figure 1The data-entry screen of the Isabel diagnosis support software. The clinician manually enters the age, gender, locality, and specialty. The query terms can be entered manually from the key findings or findings can be pasted from an existing write-up. This patient was ultimately found to have a B-cell lymphoma secreting a cryo-paraproteinFigure 2The first page of results of the Isabel diagnosis support software. Additional diagnoses are presented by selecting the ‘more diagnoses’ box
The Isabel CDSS was originally developed for use in pediatrics. In an initial evaluation, 13 clinicians (trainees and staff) at St Mary’s Hospital, London submitted a total of 99 case scenarios of hypothetical case presentations for different diagnoses, and Isabel displayed the expected diagnosis in 91% of these cases.8 Out of 100 real case scenarios gathered from 4 major teaching hospitals in the UK, Isabel suggested the correct diagnosis in 83 of 87 cases (95%). In a separate evaluation of 24 case scenarios in which the gold standard differential diagnosis was established by two senior academic pediatricians, Isabel decreased the chance of clinicians (a mix of trainees and staff clinicians) omitting a key diagnosis by suggesting a highly relevant new diagnosis in 1 of every 8 cases. In this study, the time to enter data and obtain diagnostic suggestions averaged less than 1 minute.9
The Isabel clinical diagnosis support system has now been adapted for adult medicine. The goal of this study was to evaluate the speed and accuracy of this product in suggesting the correct diagnosis in a series of complex cases in Internal Medicine.
METHOD
We considered 61 consecutive “Case Records of Massachusetts General Hospital” (New England Journal of Medicine, vol. 350:166–176, 2004 and 353:189–198, 2005). Each case had an anatomical or final diagnosis, which was considered to be correct by the discussants. We excluded 11 cases (patients under the age of 10 and cases that focused solely on management issues). The 50 remaining case histories were copied and pasted into the Isabel data-entry field. The pasted material typically included the history, physical examination findings, and laboratory test results, but data from tables and figures were not submitted. Beyond entering the patient’s age, sex, and nationality, the investigators did not attempt to otherwise tailor the search strategy. Findings were compared to the recommended (but slightly more time consuming) strategy of entering discrete key findings, as compiled by a senior internist (MLG). Because the correct diagnosis is presented at the end of each case, data entry was not blinded.
RESULTS
Using the recommended method of manually entering key findings, the list of diagnoses suggested by Isabel contained the correct diagnosis in 48 of the 50 cases (96%). Typically 3–6 key findings from each case were used. The 2 diagnoses that were not suggested (progressive multifocal encephalopathy and nephrogenic fibrosing dermopathy) were not included in the Isabel database at the time of the study; thus, these 2 cases would never have been suggested, even with different keywords.
Using the copy/paste method for entering the whole text, the list of diagnoses suggested by Isabel contained the correct diagnosis in 37 of the 50 cases (76%). Isabel presented 10 diagnoses on the first web page and 10 additional diagnoses on subsequent pages up to a total of 30 diagnoses. Because users may tend to disregard suggestions not shown on later web pages, we tracked this parameter for the copy/paste method of data entry: The correct diagnosis was presented on the first page in 19 of the 37 cases (51%) or first two pages in 28 of the 37 cases (77%). Similar data were not collected for manual data entry because the order of presentation depended on which key findings were entered.
Both data entry approaches were fast: Manually entering data and obtaining diagnostic suggestions typically required less than 1 minute per case, and the copy/paste method typically required less than 5 seconds.
DISCUSSION
Diagnostic errors are an underappreciated cause of medical error,10 and any intervention that has the potential to produce correct and timely medical diagnosis is worthy of serious consideration. Our recent analysis of diagnostic errors in Internal Medicine found that clinicians often stop thinking after arriving at a preliminary diagnosis that explains all the key findings, leading to context errors and ‘premature closure’, where further possibilities are not considered.11 These and other errors contribute to diagnoses that are wrong or delayed, causing substantial harm in the patients affected. Systems that help clinicians explore a more complete range of diagnostic possibilities could conceivably reduce these types of error.
Many different CDSSs have been developed over the years, and these typically matched the manually entered features of the case in question to a database of key findings abstracted from experts or the clinical literature. The sensitivity of these systems was in the range of 50%–60%, and the time needed to access and query the database was often several minutes.3 More recently, the possibility of using Google to search for clinical diagnoses has been suggested. However, a formal evaluation of this approach on a subset of the same “Case Records” cases used in our study found a sensitivity of 58%,12 in the range of the first-generation CDSSs and unacceptably low for clinical use.
The findings of our study indicate that CDSS products have evolved substantially. Using the Isabel CDSS, we found that data entry takes under 1 minute, and the sensitivity in a series of highly complex cases approached 100% using entry of key findings. Entry of entire case histories using copy/paste functionality allowed even faster data entry but reduced sensitivity. The loss of sensitivity seemed primarily related to negative findings included in the pasted history and physical (e.g., “the patient denies chest pain”), which are treated as positive findings (chest pain) by the search algorithm.
There are several relevant limitations of this study that make it difficult to predict how Isabel might perform as a diagnostic aid in clinical practice. First, the results obtained here reflect the theoretical upper limit of performance, given that an investigator who was aware of the correct diagnosis entered the key findings. Further, clinicians in real life seldom have the wealth of reliable and organized information that is presented in the Case Records or the time needed to use a CDSS in every case. To the extent that Isabel functions as a ‘learned intermediary’,13 success in using the program will also clearly depend on the clinical expertise of the user and their facility in working with Isabel. A serious existential concern is whether presenting a clinician with dozens of diagnostic suggestions might be a distraction or lead to unnecessary testing. We have previously identified these trade-offs as an unavoidable cost of improving patient safety: The price of improving the odds of reaching a correct diagnosis is the extra time and resources consumed in using the CDSS and considering alternative diagnoses that might turn out to be irrelevant.14
In summary, the Isabel CDSS performed quickly and accurately in suggesting correct diagnoses for complex adult medicine cases. However, the test setting was artificial, and the CDSS should be evaluated in more natural environments for its potential to support clinical diagnosis and reduce the rate of diagnostic error in medicine.

clinical diagnosis support systems
decision support
diagnostic error
isabel
internal medicine
google


Help-seeking pathways in early psychosis

Introduction Understanding the help-seeking pathways of patients with a putative risk of developing psychosis helps improving development of specialised care services. This study aimed at obtaining information about: type of health professionals contacted by patients at putative risk for psychosis on their help-seeking pathways; number of contacts; type of symptoms leading to contacts with health professionals; interval between initial contact and referral to a specialised outpatient service.
Introduction
In recent years, the aim of intervening in the early phases of psychosis has drawn great interest. Early intervention may shorten duration of untreated psychosis or even prevent onset of psychosis. Exact knowledge about the help-seeking pathways of patients is pivotal in order to provide early intervention and, thus, supply specialised and focussed health care. One study has investigated the patterns of referral in patients known to be at risk for developing a psychotic illness [1]. Further studies investigating these trajectories in first-episode patients [2–5] were able to show the important role of GPs. GPs were reported to be commonly contacted by patients who later develop psychosis. In particular, GPs were often reported to be contacted early along the help-seeking pathways and were shown to commonly be the first contacted professional group [3]. It can thus be hypothesised that a considerable part of GPs are contacted by patients who may still be presenting more insidious features such as functional disability or social withdrawal, given that these insidious features have been found to be highly prevalent in the early course of psychosis [6]. It must therefore be expected that studies of help-seeking pathways which, in addition to first-episode patients, further include patients in presumed at-risk states for psychosis would be able to corroborate the importance of GPs as early contacted professional groups. Up to date, only one other such study has been reported on patients who are considered to be at-risk for psychosis, but who have not yet developed psychosis [1]. This study is the first to compare the help-seeking behaviour of at-risk patients, patients with first psychotic episodes and of patients neither meeting clinical at-risk nor first episode criteria, but who are concerned about their mental health and thus were assessed in a prodromal service. To the best of our knowledge, it is also the first study to assess symptoms in patients at risk for psychosis at their first presentation to a prodromal service. We expected GPs to be highly represented among the early help-seeking contacts of at-risk patients.
Method
Recruitment strategy
In August 2002, a specialised, low-threshold outpatient service for the assessment of patients considered at-risk for psychosis was established in a semi-urban catchment area of North Western Switzerland (population = 300.000). This “prodromal clinic” is part of the only general psychiatric outpatient clinic of the catchment area and is associated with a public psychiatric hospital. As is the case all over Switzerland, patients can refer themselves directly to any public or private psychiatric facility and do not require referral to mental health systems via gate-keeping GPs. Patients can also be referred by any other source such as GPs, school counsellors, paediatricians or social workers.
Between August and December 2002, the majority of GPs (n = 240), private psychiatrists and psychologists (n = 130) of the catchment area were educated either individually or in small groups about the early warning signs of emerging psychosis and manifest psychotic features. Education strategies were based on the findings from a large, nation-wide survey among 1089 GPs [7] that showed that GPs often underidentified the insidious features of beginning psychosis, such as functional decline. In keeping with these findings, education strategies primarily focussed on the importance of the insidious features of beginning psychosis such as sustained functional decline and social withdrawal as important early warning signs. With the inclusion of the adolescent psychiatric service into our prodromal clinic in January 2004, all private youth psychiatrists as well as paediatricians were equally enrolled in this campaign so as to ensure additional referrals of patients aged 14–17 years, as symptom onset in this young age group has been associated with less favourable outcomes [8].
In our study, sensitised primary and secondary health care providers were encouraged to contact the outpatient clinic whenever one of their patients showed sustained decline in social functioning in a still relatively asymptomatic state; if they showed attenuated or brief intermittent positive psychotic symptoms; or if they suspected that “something odd had been going on” for some time in their patients.
The research protocol was approved by the “Ethikkommission beider Basel”. It was designed to obtain data on help-seeking pathways and to collect longitudinal information about progression of symptoms and social as well as neuropsychological functioning, and was not a treatment trial.
Patient sample
As of January 1st 2003, patients could be referred to our prodromal clinic. Consenting patients were included in the study. Each individual was fully informed about the research protocol. Written informed consent was obtained from all patients, and additionally from the parents if under 18. Patients were considered ineligible for further assessment within the study if they presented a history of a past psychotic episode, traumatic brain injury, epilepsy or other known neurological disorder; other significant medical conditions considered to affect cognitive performance and self-perception; an IQ of below 70; or patients below the age of 18 in the first year and below the age of 14 years in the second year of the present study.
Symptom ratings
We assessed the entire range of potential symptoms from early prodrome to first psychotic episode using the following instruments: potential early prodromes were assessed using the Schizophrenia Prediction Instrument—Adult Version (SPI-A) [9], potential late prodromes using the Scale of Prodromal Symptoms (SOPS) with its companion interview manual (Structured Interview for Prodromal Symptoms, SIPS) [10, 11]. For the assessment of potentially manifest psychosis, we used the Positive and Negative Syndrome Scale (PANSS; 12). Ratings were performed by an experienced consultant research psychiatrist (A.S.) or by a trained masters-level psychologist (D.D.). Interrater reliability was established by extensive training by one of the authors of the SPI-A and by repeated training sessions involving all raters. However, formal assessment of interrater reliability was not conducted.
The SPI-A has been developed from a hierarchical cluster analysis of the BSABS [13] and includes those 10 basic symptoms for which a good positive predictive value for later schizophrenia has previously been reported [14, 15]. These basic symptoms include: thought interferences; thought perseveration; thought pressure; thought blockages; disturbances of receptive language; decreased ability to discriminate between ideas and perception, phantasy and true memory; unstable ideas of reference; derealisation; visual and acoustic perception disturbances.
The structure of the SIPS manual and the SOPS rating scale as well as its high validity and reliability have been reported elsewhere [10, 11]. In summary, these authors classified patients with either attenuated positive psychotic symptoms, brief limited intermittent positive psychotic symptoms or with a combined functional decline and genetic high-risk as ultra high-risk (UHR).
Group assignment
Depending on symptom severity, patients were assigned to one of the three following groups: (1) the First Episode (FE) group was constituted by patients scoring 6 on any of the five SOPS positive symptom items for more than 1 week, which is identical with a minimum score of 4 on any of the PANSS positive items for the same time period. (2) The at-risk (AR) group included: patients meeting severity, but not duration criteria of the FE group; patients meeting scores of 3–5 on any SOPS positive psychotic symptom item at least once a week over the past month; patients with a first degree relative with any psychotic disorder and/or patient meeting DSM-IV Schizotypal Personality Disorder criteria and a 30% or greater drop in the GAF [16] score during the last month compared to 12 months ago; patients scoring a minimum of 3 on any of the 10 predictive basic symptoms according to the SPI-A. (3) Help-seeking patients who were referred to the prodromal clinic for risk assessment, but who did neither meet FE nor AR criteria, were assigned to the Patient Control (PCo) group.
Assessment of help-seeking pathways and of symptoms at prior contacts
After symptom assessment, patients were asked which other professional groups they had previously contacted for similar problems. Further, timing and number of contacts as well as type of symptom leading to each single prior contact were recorded and, whenever possible, corroborated with information from family members. These data were assessed using a semi-structured interview that was designed for this study. However, no information was collected whether referrals to mental health services were voluntary or involuntary.
Analysis
Data on patients who were enrolled between January 2003 and December 2004 were analysed using the computer package SPSS Version 11.04. We compared number of contacts and professionals contacted along the help-seeking pathways between groups. Similarly, symptoms that patients had presented during their previous help-seeking contacts and the interval between initial contact and referral to our prodromal service were compared between groups. Continuous variables were compared with t-tests or ANOVA, categorical variables with χ2 tests or, if ranked, with Kruskal–Wallis and Mann–Whitney-U tests. An alpha level of 0.05 was considered significant.
Results
Sample characteristics
Of the 104 patients included into this study, 28 were female and 76 male. The mean age was 23.2 years (SD: ±5.43; range: 16–38) in the first year of the study, and 20.0 years (SD: ±6.08; range: 14–40) in the second year. 28 patients met criteria for the FE group, 50 for the AR group, and 26 patients were assigned to the PCo group. A summary of the socio-demographic characteristics across these study groups is shown in Table 1.
Table 1Socio-demographic characteristicsFirst episodeAt-riskControlsTotalSignificance valuesNumber of patients285026104Mean age23.42122.122χ² = 3.744, df = 2, P = 0.153Gender    m (%)22 (79)31 (62)23 (88)76 (73)χ² = 6.676, df = 2, P = 0.036    f (%)6 (21)19 (38)3 (12)28 (27)Marriage status    Unmarried (%)26 (93)45 (90)25 (96)96 (92)χ² = 0.929, df = 2, P = 0.629Children    No children (%)28 (100)48 (96)25 (96)101 (97)χ² = 1.140, df = 2, P = 0.566Living situation    Alone (%)5 (18)7 (14)6 (23)18 (17)χ² = 8.412, df = 4, P = 0.078    With partner or friends (%)5 (18)5 (10)8 (31)18 (17)    With parents or relatives (%)18 (64)36 (72)10 (39)64 (62)City size    >200,000 (%)5 (18)5 (10)4 (15)14 (14)χ² = 3.337, df = 6, P = 0.766    10,000–200,000 (%)7 (25)12 (24)6 (23)25 (24)    <10,000 (%)16 (57)33 (66)16 (62)59 (62)School education    None completed (%)1 (4)1 (2)2 (8)4 (4)χ² = 4.688, df = 6, P = 0.584    Obligatory school (%)19 (68)36 (72)18 (69)73 (70)    Maturity (%)5 (18)4 (8)4 (15)13 (13)    Still in education (%)3 (11)9 (18)2 (8)14 (13)Professional training    None (%)9 (32)22 (44)8 (31)39 (38)χ² = 10.429, df = 6, P = 0.108    Apprenticeship (%)12 (42)18 (36)12 (46)42 (40)    Academic education (%)5 (18)1 (2)1 (4)7 (7)    Still in training (%)2 (7)9 (18)5 (19)16 (15)Work situation    No occupation (%)12 (43)19 (38)10 (39)41 (39)χ² = 4.097, df = 6, P = 0.664    At work (%)10 (36)11 (22)6 (22)27 (26)    In training (%)6 (21)20 (40)10 (39)36 (35)
Number of contacts and professionals contacted
The mean number of contacts over all three patient groups was 2.38 (SD: ±1.42; median: 3.0; range: 1–8) with no significant between-group differences (χ2 = 208.375, df = 2, P = 0.605). The overall patient sample reported a total of 247 contacts prior to their referral to our prodromal clinic. Table 2 shows the type of professional groups contacted by the patients across the three study groups along their help-seeking trajectories. FE patients more often presented themselves to mental health professionals (psychiatrists, psychologists as well as psychiatric out- and in-patient services) than AR and PCo patients (71.2% vs. 57.6% vs. 53.5% of contacts); however, this comparison did not reach significance (χ2 = 4.724, df = 2, P = 0.094). When comparing FE patients with the combined non-psychotic AR and PCo patients, FE patients significantly more often presented themselves to mental health professionals (χ2 = 4.461, df = 1, P = 0.024). Furthermore, when comparing the numbers of contacts with mental health professionals (149/247 = 60.3%) with those to non-mental health professionals (98/247 = 39.7%), FE patients more often contacted in-patient services, whereas PCo and AR patients more often attended GPs and “other” professionals such as alternative medical practitioners, non-medical counselling services and non-specified professionals (χ2 = 20.189, df = 8, P = 0.010). Of the overall sample, 51.0% (53/104) contacted a GP at some point in time, whereas 83.7% had at least once visited a mental health professional.
Table 2Distribution of professionals contactedFEARPCoTotal(n) % of total(66) 26.7(125) 50.6(56) 22.7(247) 100General practitioners21.2% (14)21.6% (27)37.5% (21)25.1% (62)Private Psychiatrists/Psychologists21.2% (14)24.8% (31)21.4% (12)23.1% (57)Psychiatric out-patient services30.3% (20)25.6% (32)16.1% (9)24.7% (61)Psychiatric in-patient services19.7% (13)7.2% (9)16.1% (9)12.6% (31)Other professionals7.6% (5)20.8% (26)8.9% (5)14.6% (36)Percentages (and absolute numbers) of contacts made with professional groups at any stage of the help-seeking pathway
However, as shown in Fig. 1, there was a trend for progression from non-psychiatric towards psychiatric services. Contacts with GPs were often at an earlier stage on the help-seeking pathways. Moreover, GPs were the most often first-contacted professional groups, whereas psychiatric in-patient services were most commonly the last contacted professional group (χ2 = 15.816, df = 4, P = 0.003). However, differences among study groups did not reach significance (first contacts: χ2 = 13.102, df = 8, P = 0.108; last contacts: χ2 = 9.699, df = 8, P = 0.287).
Fig. 1Progression to specialized services. Distribution of first (n = 104) and last contacts (n = 104) in percent made to different helper groups. Contacts representing the only help-seeking attempt by a subject appear in both first and last contacts
Symptoms at prior presentations
The complete set of signs and symptoms which led patients or their relatives to seek help at each of the recorded contacts was obtained from 100 of the total patient sample (96.2%). The frequencies of the most commonly presented symptoms are shown in Table 3. In the AR and PCo groups, the trias depression, social decline and social withdrawal were presented most frequently. In the FE group, unusual thought content was the most frequent symptom, followed by the above-mentioned triad.
Table 3Frequency of presented symptomsFEARPCoTotalNumber of contacts6612556247Positive symptoms  Ideas of reference (%)15 (23.1)2 (<5.0)1 (<5.0)18 (7.7)  Unusual contents of thought (%)36 (55.4)18 (14.8)1 (<5.0)55 (23.5)  Hallucinations (%)13 (20.0)11 (9.0)2 (<5.0)26 (11.1)  Perceptual disturbances (%)5 (7.7)21 (17.2)026 (11.1)  Alienation or derealisation (%)16 (24.6)29 (23.8)045 (19.2)Negative symptoms  Deterioration of social functioning (%)32 (49.2)54 (44.3)16 (34.0)102 (43.6)  Social withdrawal (%)32 (49.2)42 (34.4)14 (29.8)88 (37.6)  Avolition (%)9 (13.8)26 (21.3)12 (25.5)47 (20.1)Cognitive symptoms  Impaired concentration (%)13 (20.0)29 (23.8)1 (<5.0)43 (18.4)  Impaired attention (%)7 (10.8)12 (9.8)1 (<5.0)20 (8.5)  Impaired memory (%)10 (15.4)5 (<5)2 (<5.0)17 (7.3)  Formal thought disorders (%)17 (26.2)25 (20.5)042 (17.9)Other symptoms  Depression (%)24 (36.9)74 (60.7)38 (80.9)136 (58.1)  Anxiety (%)14 (21.5)27 (22.1)9 (19.1)50 (21.4)  Lack of impulse-control (%)2 (<5.0)14 (<5.0)11 (23.4)27 (11.5)Absolute numbers (percentages) of presented symptoms. Percentage values do not add to 100% due to multiple symptoms recorded for most contacts
FE patients had presented at least one positive symptom in 80.3% of their prior contacts, AR patients in 56.6% and PCo patients in 5.9% of their prior contacts (χ2 = 65.680, df = 2, P < 0.001). Similarly, when comparing numbers of patients rather than numbers of contacts, 89% FE, 65% AR and 8% PCo patients had reported positive symptoms at any stage along their help-seeking pathway (χ2 = 35.483, df = 2, P < 0.001). Of patients presenting positive symptoms along their help-seeking pathways, 81.2% already did so during their first contact. Positive symptoms were significantly more often reported in first help-seeking contacts with psychiatric out-patient (63.6%) and in-patient (100%) services as compared to GPs (37.1%) and private psychiatrists/psychologists (31.8%) (χ2 = 13.425, df = 4, P = 0.009).
Table 4 shows the relationship between positive and non-positive symptoms and the professional groups to whom they were presented. Positive symptoms were significantly more often reported in contacts with both psychiatric out- and in-patient services (67.2%, 70.0%) as compared to GPs, who were contacted for positive symptoms only in 37.3% recorded contacts. Consequently, GPs were contacted with more unspecific symptoms (62.7%) such as depression and negative symptoms (χ2 = 18.046, df = 4, P = 0.01).
Table 4Positive vs. non-positive symptoms presented to professional groupsGeneral practitionersPsychiatrists psychologistsOut-patient servicesIn-patient servicesOther xprofessionalsTotalNumber of contacts5955613034239Contacts made with positive symptoms37.3% (22)40% (22)67.2% (41)70% (21)55.9% (19)(125)Contacts made with non-positive symptoms62.7% (37)60% (33)32.8% (20)30.0% (9)44.1% (15)(114)Percentages (absolute numbers) of symptoms presented to professional groups. Percentage values do not add to 100% due to multiple symptoms recorded for most contactsPositive symptoms included items according to Table 3, non-positive symptoms included items according to “negative”, “cognitive” or “other” symptoms in Table 3
Duration from initial contact to referral
The median interval from initial contact to time of referral to our prodromal clinic was 36 weeks (range: 1 day to 7.6 years; mean: 124.0 weeks; ±SD 217.1). About a third (33.3%) of the patients whose initial contact was over 18 months before referral to the prodromal clinic accounted for the relatively long mean help-seeking duration. Two-thirds (66.6%), however, were referred within 18 months, with one-third (35.4%) being referred within 2 months. The median help-seeking duration of the FE group (12.5 weeks) was shorter than for the AR (42.5 weeks) and PCo groups (46.5 weeks), however, between-group comparisons dropped just below level of significance (z = −1.879, P = 0.06). More significantly, patients presenting with positive symptoms at some stage along their help-seeking trajectories (n = 56) showed shorter pathway durations (median = 24.5 weeks) than patients presenting only with negative, affective or “unspecific” symptoms, i.e. patients never presenting positive symptoms (n = 44; median = 46.0 weeks) (z = −2.072, P = 0.038).
We further calculated the median intervals from any of the prior contacts to the time of referral and compared these “delays to referral” for the different professional groups that were contacted. Median “referral delay” across all three-study groups was 28 weeks with no between-group differences (z = −1.260, P = 0.208). Median “delays” were 15 weeks for contacts with GPs, 68.5 weeks for private psychiatrists and psychologists, each 13.0 weeks for psychiatric in- and out-patient services, and 52.2 weeks for contacts made to “others” (z = −3.554, P < 0.001). For 165 of the 247 recorded contacts, we were able to obtain information on whether a continuous treatment of three or more consecutive sessions was provided. The significantly longer “delays to referral” following contacts with private psychiatrists and psychologists was associated with the finding that in 58% of all contacts made with these groups, three or more sessions were administered. In comparison, in the vast majority of the cases (83.7%) contacts with GPs were single consultations.
Discussion
To the best of our knowledge, this is the first study to investigate the help-seeking pathways of a patient cohort, which was referred to a prodromal clinic for a suspected at-risk state for psychosis. We were able to confirm the important role of GPs along the help-seeking pathways of patients with emerging psychosis. Furthermore, GPs were contacted in particular by those patients who presented insidious features. It was these patients that showed the longest delays in referral to our specialised outpatient service.
The importance of GPs along the early pathways
This study was able to confirm recent reports from Australia [1] and from Germany [17, 18] that a substantial number of contacts made along the help-seeking pathways were with mental health care professionals. As the gatekeeping model applies for Australia, but not for Germany or Switzerland, mental health care professionals seem to play an important role independent of a particular health system. At the same time, our study also confirms earlier reports [2–5] about the pivotal role of GPs in the early pathways to care of help-seeking individuals that were referred to our prodromal clinic for an assessment of a potential psychotic at-risk state. While they were solicited in half of all contacts, GPs even constituted the most frequently first-contacted professional group (34.6%). It is noteworthy that in Switzerland patients can refer themselves to GPs without a “gate-keeping” GP. It can be thus assumed that in health systems with “gate-keeping” GPs [19], the present results may be even more representative.
The challenge of detecting the insidious symptoms
In addition, our results revealed that patients in less symptomatic states more commonly seek help with their GPs. Patients with manifest psychotic symptoms, however, more often contacted specialists. These results are paralleled by the finding that a shift from contacts made with primary carers to contacts with more specialised professional groups is taking place along the pathways (as shown in Fig. 1). Similar findings were reported by Lincoln et al. [3]. We assume that this process may also be an expression of the symptom progression along the course of early psychosis [20, 21].
As we thus had expected, GPs are faced with the difficult task to detect potential at-risk states in patients that do not yet present psychotic, but the unspecific insidious features. This finding is of particular relevance as a recent comprehensive survey among 1089 Swiss GPs was able to show that GPs commonly under-identified the insidious features of emerging psychosis [7]. Preliminary results of an international replication study (IGPS) of the Swiss survey across 10 countries were able to confirm these findings [22]. Although features such as functional disability or social isolation may not necessarily lead to overt psychosis and may either be expression of another psychiatrically relevant process or remit after time, it is the detection of the earliest signs and symptoms of emerging psychosis that has become the aim of preventive efforts. Similarly, depression has been shown to be highly prevalent in emerging psychosis [6]. While this triad—functional disability, social isolation and depression—was found to be highly prevalent across all groups in the present study, it is also a characteristic of the deficit syndrome of schizophrenia [23]. If such symptoms are true-positive precursors of later psychosis, the non-detection of such patients contributes to a substantial delay on their way to specialised services and adequate treatment, which in turn has been shown to negatively affect the outcome of patients with first-episode psychosis [24]. Accordingly, our study confirmed earlier reports that patients with more insidious features showed longer pathway durations than patients with predominant positive symptoms [25]. Deficit syndrome patients, however, may show lower adherence to treatment and are per se characterised by worse outcomes [23].
Delays in referral
Interestingly, when compared to specialists and other professional groups, GPs referred patients more rapidly to other professional groups once they were contacted. In contrast, more contacts per patient were found with private psychiatrists and psychologists before final referral to the prodromal clinic was established. Partially, this finding may be explained by our large and repetitious sensitisation of GPs about the insidious features of early psychosis. However, some of the sampled patients had contacted a GP before the sensitisation had taken place, suggesting that GPs tend to refer such patients more rapidly. This would be in line with the findings from both the Swiss survey [7] and its international replication study [22], in which GPs indicated that they wished to rapidly refer patients in whom they suspected a beginning psychosis to specialised outpatient services. Given their degree of specialty, private psychiatrists and psychologists, in contrast, seem not to engage into rapid referral of these patients.
Given the considerable potential for recall bias, our study did not assess type of treatment that was provided to patients at each of their contacts. Thus, no conclusions can be drawn about the adequacy of applied treatment strategies. It may be the psychiatrists and psychologists that are seeing the more symptomatic and “difficult-to-treat” patients who require a stable and continuous treatment setting. However, all patients included into this study were finally referred to our prodromal clinic, which may have either taken place for diagnostic assessment or for optimising treatment. It may thus be suggested that referrals of patients could have ideally been initiated already at an earlier stage along their help-seeking pathways. Interestingly, in two German studies on the pathways to care of patients with first-episode psychosis, contacts with private psychiatrists and psychologist were associated with longer duration of untreated psychosis as compared to GPs and general casualty services [17, 18]. Taken together, these findings suggest that education may not only need to include primary carers such as GPs, but also other professional groups as well as secondary carers such as private psychiatrists and psychologists.
Number of contacts and duration of help-seeking pathways
Finally, the comparatively low mean number of contacts (2.38) on the pathways to care over all patient groups may express the effect of the large sensitisation of professional groups that was conducted when our prodromal clinic was established. This finding is identical with the Phillips et al. study [1] who reported a mean number of contacts of 2.36 in their at-risk sample. Additionally, the median duration from initial contact to referral to our prodromal service was very similar to the one reported by Phillips et al. [1] (42.5 vs. 41.4 weeks). In those studies assessing help-seeking pathways of first-episode cohorts, only one other study reported lower mean contacts (1.7; range 1–4) and also was conducted as part of an early psychosis service [5]. In comparison, number of contacts of patient samples not treated in specialised services were reported to be higher. A mean number of contact of 4.9 was reported by Johnstone et al. [2], a finding which was identically reported in a study that included patients treated before and after the establishment of an early psychosis service [3]. These findings point to the importance of specialised early psychosis services.
Weaknesses of this paper
Finally, a few weaknesses of this survey should be acknowledged. First, this study, like any retrospective research, is limited by an inherent potential for recall bias. Given the considerable length of pathway duration in the case of some patients, information on the exact number and on timing of contacts may be subject to errors. Ideally, all contacted professional groups would need to be contacted and interviewed in order to validate the information obtained by patients and their relatives. Second, pathways were studied on a patient sample, which was mainly constituted by subjects without manifest psychosis. Contacted professional groups may therefore not have been alarmed to refer their patients, and referral to our prodromal clinic may have only occurred in order to exclude an at-risk state for psychosis once the prodromal clinic was established. It can, however, be suggested that referrals would only take place if there is particular need for further assessment. Moreover, referrals may also have been initiated by patients themselves, although we did not control for that aspect in our study. Third, we cannot exclude that pathways may differ in patients that are never referred to our prodromal clinic. It may well be possible that an unknown number of patients in presumed at-risk states are treated by private specialists and undergo complete remission. It may therefore be a per se selection of more impaired patients that will be referred to a prodromal clinic. Although the at-risk criteria applied in the current study are now well-established in the early psychosis research community [26], it may well be possible that not all true at-risk patients meet these criteria. Thus, our study may have only captured a fraction of the individuals developing psychosis [27]. Fourth, we did not control for patient-related factors that may in a large part contribute to referral delays. These factors may include lack of insight, poor social adjustment, paranoid thoughts or avolition. Finally, the training and role of GPs in the health care system may vary across countries; thus, not all of the findings of our study may be generalisable to other settings. However, our findings emerge from a health system where specialists may be contacted without the referral of the “gate-keeping” GPs. Thus, in health systems with gate-keeping models our findings that GPs are more commonly contacted by individuals with insidious features may warrant appropriate education efforts even stronger.
Conclusions
Our study confirms earlier reports of the GPs’ important role on the help-seeking pathways of psychotic patients. Furthermore, our study revealed that GPs are often contacted by patients with suspected at-risk states for psychosis early on their pathways to care. The insidious nature of the symptoms presented by these patients in the early stages of their pathways imposes an important challenge for GPs, as insidious features of early psychotic stages are difficult to detect. Our results thus support the need for appropriate education of GPs on these insidious features and for rapidly accessible specialised outpatient services. In a few countries, such specialized outpatient services have been established in recent years [28]. For example, in England, under the National Health Service Plan, 50 early intervention teams have been established at a cost of £70 million [29]. It can thus be expected that help-seeking pathways of patients in early phases of psychosis may be reduced, thus maybe improving the overall outcome of patients.

help-seeking pathways
early psychosis
first episode
prodrome
schizophrenia